This PR resolves two issues:
First, expanding * inside aggregate functions of structs when using Dataframe/Dataset APIs. For example,
```scala
structDf.groupBy($"a").agg(min(struct($"record.*")))
```
Second, it improves the error messages when having invalid star usage when using Dataframe/Dataset APIs. For example,
```scala
pagecounts4PartitionsDS
.map(line => (line._1, line._3))
.toDF()
.groupBy($"_1")
.agg(sum("*") as "sumOccurances")
```
Before the fix, the invalid usage will issue a confusing error message, like:
```
org.apache.spark.sql.AnalysisException: cannot resolve '_1' given input columns _1, _2;
```
After the fix, the message is like:
```
org.apache.spark.sql.AnalysisException: Invalid usage of '*' in function 'sum'
```
cc: rxin nongli cloud-fan
Author: gatorsmile <gatorsmile@gmail.com>
Closes#11208 from gatorsmile/sumDataSetResolution.
## What changes were proposed in this pull request?
There is only one exception: `PythonUDF`. However, I don't think the `PythonUDF#` prefix is useful, as we can only create python udf under python context. This PR removes the `PythonUDF#` prefix from `PythonUDF.toString`, so that it doesn't need to overrde `sql`.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11859 from cloud-fan/tmp.
## What changes were proposed in this pull request?
This is a more aggressive version of PR #11820, which not only fixes the original problem, but also does the following updates to enforce the at-most-one-qualifier constraint:
- Renames `NamedExpression.qualifiers` to `NamedExpression.qualifier`
- Uses `Option[String]` rather than `Seq[String]` for `NamedExpression.qualifier`
Quoted PR description of #11820 here:
> Current implementations of `AttributeReference.sql` and `Alias.sql` joins all available qualifiers, which is logically wrong. But this implementation mistake doesn't cause any real SQL generation bugs though, since there is always at most one qualifier for any given `AttributeReference` or `Alias`.
## How was this patch tested?
Existing tests should be enough.
Author: Cheng Lian <lian@databricks.com>
Closes#11822 from liancheng/spark-14004-aggressive.
## What changes were proposed in this pull request?
case classes defined in REPL are wrapped by line classes, and we have a trick for scala 2.10 REPL to automatically register the wrapper classes to `OuterScope` so that we can use when create encoders.
However, this trick doesn't work right after we upgrade to scala 2.11, and unfortunately the tests are only in scala 2.10, which makes this bug hidden until now.
This PR moves the encoder tests to scala 2.11 `ReplSuite`, and fixes this bug by another approach(the previous trick can't port to scala 2.11 REPL): make `OuterScope` smarter that can detect classes defined in REPL and load the singleton of line wrapper classes automatically.
## How was this patch tested?
the migrated encoder tests in `ReplSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11410 from cloud-fan/repl.
## What changes were proposed in this pull request?
When we validate an encoder, we may call `dataType` on unresolved expressions. This PR fix the validation so that we will resolve attributes first.
## How was this patch tested?
a new test in `DatasetSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11816 from cloud-fan/encoder.
#### What changes were proposed in this pull request?
This PR is to support order by position in SQL, e.g.
```SQL
select c1, c2, c3 from tbl order by 1 desc, 3
```
should be equivalent to
```SQL
select c1, c2, c3 from tbl order by c1 desc, c3 asc
```
This is controlled by config option `spark.sql.orderByOrdinal`.
- When true, the ordinal numbers are treated as the position in the select list.
- When false, the ordinal number in order/sort By clause are ignored.
- Only convert integer literals (not foldable expressions). If found foldable expressions, ignore them
- This also works with select *.
**Question**: Do we still need sort by columns that contain zero reference? In this case, it will have no impact on the sorting results. IMO, we should not allow users do it. rxin cloud-fan marmbrus yhuai hvanhovell
-- Update: In these cases, they are ignored in this case.
**Note**: This PR is taken from https://github.com/apache/spark/pull/10731. When merging this PR, please give the credit to zhichao-li
Also cc all the people who are involved in the previous discussion: adrian-wang chenghao-intel tejasapatil
#### How was this patch tested?
Added a few test cases for both positive and negative test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#11815 from gatorsmile/orderByPosition.
## What changes were proposed in this pull request?
[Spark Coding Style Guide](https://cwiki.apache.org/confluence/display/SPARK/Spark+Code+Style+Guide) has 100-character limit on lines, but it's disabled for Java since 11/09/15. This PR enables **LineLength** checkstyle again. To help that, this also introduces **RedundantImport** and **RedundantModifier**, too. The following is the diff on `checkstyle.xml`.
```xml
- <!-- TODO: 11/09/15 disabled - the lengths are currently > 100 in many places -->
- <!--
<module name="LineLength">
<property name="max" value="100"/>
<property name="ignorePattern" value="^package.*|^import.*|a href|href|http://|https://|ftp://"/>
</module>
- -->
<module name="NoLineWrap"/>
<module name="EmptyBlock">
<property name="option" value="TEXT"/>
-167,5 +164,7
</module>
<module name="CommentsIndentation"/>
<module name="UnusedImports"/>
+ <module name="RedundantImport"/>
+ <module name="RedundantModifier"/>
```
## How was this patch tested?
Currently, `lint-java` is disabled in Jenkins. It needs a manual test.
After passing the Jenkins tests, `dev/lint-java` should passes locally.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11831 from dongjoon-hyun/SPARK-14011.
#### What changes were proposed in this pull request?
This PR is to add a new Optimizer rule for pruning Sort if its SortOrder is no-op. In the phase of **Optimizer**, if a specific `SortOrder` does not have any reference, it has no effect on the sorting results. If `Sort` is empty, remove the whole `Sort`.
For example, in the following SQL query
```SQL
SELECT * FROM t ORDER BY NULL + 5
```
Before the fix, the plan is like
```
== Analyzed Logical Plan ==
a: int, b: int
Sort [(cast(null as int) + 5) ASC], true
+- Project [a#92,b#93]
+- SubqueryAlias t
+- Project [_1#89 AS a#92,_2#90 AS b#93]
+- LocalRelation [_1#89,_2#90], [[1,2],[1,2]]
== Optimized Logical Plan ==
Sort [null ASC], true
+- LocalRelation [a#92,b#93], [[1,2],[1,2]]
== Physical Plan ==
WholeStageCodegen
: +- Sort [null ASC], true, 0
: +- INPUT
+- Exchange rangepartitioning(null ASC, 5), None
+- LocalTableScan [a#92,b#93], [[1,2],[1,2]]
```
After the fix, the plan is like
```
== Analyzed Logical Plan ==
a: int, b: int
Sort [(cast(null as int) + 5) ASC], true
+- Project [a#92,b#93]
+- SubqueryAlias t
+- Project [_1#89 AS a#92,_2#90 AS b#93]
+- LocalRelation [_1#89,_2#90], [[1,2],[1,2]]
== Optimized Logical Plan ==
LocalRelation [a#92,b#93], [[1,2],[1,2]]
== Physical Plan ==
LocalTableScan [a#92,b#93], [[1,2],[1,2]]
```
cc rxin cloud-fan marmbrus Thanks!
#### How was this patch tested?
Added a test suite for covering this rule
Author: gatorsmile <gatorsmile@gmail.com>
Closes#11840 from gatorsmile/sortElimination.
## What changes were proposed in this pull request?
Current implementations of `AttributeReference.sql` and `Alias.sql` joins all available qualifiers, which is logically wrong. But this implementation mistake doesn't cause any real SQL generation bugs though, since there is always at most one qualifier for any given `AttributeReference` or `Alias`.
This PR fixes this issue by only picking the first qualifiers.
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
Existing tests should be enough.
Author: Cheng Lian <lian@databricks.com>
Closes#11820 from liancheng/spark-14004-single-qualifier.
JIRA: https://issues.apache.org/jira/browse/SPARK-13838
## What changes were proposed in this pull request?
We should also clear the variable code in `BoundReference.genCode` to prevent it to be evaluated twice, as we did in `evaluateVariables`.
## How was this patch tested?
Existing tests.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#11674 from viirya/avoid-reevaluate.
## What changes were proposed in this pull request?
Support queries that JOIN tables with USING clause.
SELECT * from table1 JOIN table2 USING <column_list>
USING clause can be used as a means to simplify the join condition
when :
1) Equijoin semantics is desired and
2) The column names in the equijoin have the same name.
We already have the support for Natural Join in Spark. This PR makes
use of the already existing infrastructure for natural join to
form the join condition and also the projection list.
## How was the this patch tested?
Have added unit tests in SQLQuerySuite, CatalystQlSuite, ResolveNaturalJoinSuite
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#11297 from dilipbiswal/spark-13427.
## What changes were proposed in this pull request?
Logging was made private in Spark 2.0. If we move it, then users would be able to create a Logging trait themselves to avoid changing their own code.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11764 from cloud-fan/logger.
## What changes were proposed in this pull request?
Fix expression generation for optional types.
Standard Java reflection causes issues when dealing with synthetic Scala objects (things that do not map to Java and thus contain a dollar sign in their name). This patch introduces Scala reflection in such cases.
This patch also adds a regression test for Dataset's handling of classes defined in package objects (which was the initial purpose of this PR).
## How was this patch tested?
A new test in ExpressionEncoderSuite that tests optional inner classes and a regression test for Dataset's handling of package objects.
Author: Jakob Odersky <jakob@odersky.com>
Closes#11708 from jodersky/SPARK-13118-package-objects.
## What changes were proposed in this pull request?
We need to copy the UnsafeRow since a Join could produce multiple rows from single input rows. We could avoid that if there is no join (or the join will not produce multiple rows) inside WholeStageCodegen.
Updated the benchmark for `collect`, we could see 20-30% speedup.
## How was this patch tested?
existing unit tests.
Author: Davies Liu <davies@databricks.com>
Closes#11740 from davies/avoid_copy2.
## What changes were proposed in this pull request?
As part of the effort to merge `SQLContext` and `HiveContext`, this patch implements an internal catalog called `SessionCatalog` that handles temporary functions and tables and delegates metastore operations to `ExternalCatalog`. Currently, this is still dead code, but in the future it will be part of `SessionState` and will replace `o.a.s.sql.catalyst.analysis.Catalog`.
A recent patch #11573 parses Hive commands ourselves in Spark, but still passes the entire query text to Hive. In a future patch, we will use `SessionCatalog` to implement the parsed commands.
## How was this patch tested?
800+ lines of tests in `SessionCatalogSuite`.
Author: Andrew Or <andrew@databricks.com>
Closes#11750 from andrewor14/temp-catalog.
## What changes were proposed in this pull request?
Narrow down the parameter type of `UserDefinedType#serialize()`. Currently, the parameter type is `Any`, however it would logically make more sense to narrow it down to the type of the actual user defined type.
## How was this patch tested?
Existing tests were successfully run on local machine.
Author: Jakob Odersky <jakob@odersky.com>
Closes#11379 from jodersky/SPARK-11011-udt-types.
## What changes were proposed in this pull request?
**[I'll link it to the JIRA once ASF JIRA is back online]**
This PR modifies the existing `CombineFilters` rule to remove redundant conditions while combining individual filter predicates. For instance, queries of the form `table.where('a === 1 && 'b === 1).where('a === 1 && 'c === 1)` will now be optimized to ` table.where('a === 1 && 'b === 1 && 'c === 1)` (instead of ` table.where('a === 1 && 'a === 1 && 'b === 1 && 'c === 1)`)
## How was this patch tested?
Unit test in `FilterPushdownSuite`
Author: Sameer Agarwal <sameer@databricks.com>
Closes#11670 from sameeragarwal/combine-filters.
## What changes were proposed in this pull request?
This PR generalizes the `NullFiltering` optimizer rule in catalyst to `InferFiltersFromConstraints` that can automatically infer all relevant filters based on an operator's constraints while making sure of 2 things:
(a) no redundant filters are generated, and
(b) filters that do not contribute to any further optimizations are not generated.
## How was this patch tested?
Extended all tests in `InferFiltersFromConstraintsSuite` (that were initially based on `NullFilteringSuite` to test filter inference in `Filter` and `Join` operators.
In particular the 2 tests ( `single inner join with pre-existing filters: filter out values on either side` and `multiple inner joins: filter out values on all sides on equi-join keys` attempts to highlight/test the real potential of this rule for join optimization.
Author: Sameer Agarwal <sameer@databricks.com>
Closes#11665 from sameeragarwal/infer-filters.
## What changes were proposed in this pull request?
This PR tries to solve a fundamental issue in the `SQLBuilder`. When we want to turn a logical plan into SQL string and put it after FROM clause, we need to wrap it with a sub-query. However, a logical plan is allowed to have same-name outputs with different qualifiers(e.g. the `Join` operator), and this kind of plan can't be put under a subquery as we will erase and assign a new qualifier to all outputs and make it impossible to distinguish same-name outputs.
To solve this problem, this PR renames all attributes with globally unique names(using exprId), so that we don't need qualifiers to resolve ambiguity anymore.
For example, `SELECT x.key, MAX(y.key) OVER () FROM t x JOIN t y`, we will parse this SQL to a Window operator and a Project operator, and add a sub-query between them. The generated SQL looks like:
```
SELECT sq_1.key, sq_1.max
FROM (
SELECT sq_0.key, sq_0.key, MAX(sq_0.key) OVER () AS max
FROM (
SELECT x.key, y.key FROM t1 AS x JOIN t2 AS y
) AS sq_0
) AS sq_1
```
You can see, the `key` columns become ambiguous after `sq_0`.
After this PR, it will generate something like:
```
SELECT attr_30 AS key, attr_37 AS max
FROM (
SELECT attr_30, attr_37
FROM (
SELECT attr_30, attr_35, MAX(attr_35) AS attr_37
FROM (
SELECT attr_30, attr_35 FROM
(SELECT key AS attr_30 FROM t1) AS sq_0
INNER JOIN
(SELECT key AS attr_35 FROM t1) AS sq_1
) AS sq_2
) AS sq_3
) AS sq_4
```
The outermost SELECT is used to turn the generated named to real names back, and the innermost SELECT is used to alias real columns to our generated names. Between them, there is no name ambiguity anymore.
## How was this patch tested?
existing tests and new tests in LogicalPlanToSQLSuite.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11658 from cloud-fan/gensql.
## What changes were proposed in this pull request?
There is a feature of hive SQL called multi-insert. For example:
```
FROM src
INSERT OVERWRITE TABLE dest1
SELECT key + 1
INSERT OVERWRITE TABLE dest2
SELECT key WHERE key > 2
INSERT OVERWRITE TABLE dest3
SELECT col EXPLODE(arr) exp AS col
...
```
We partially support it currently, with some limitations: 1) WHERE can't reference columns produced by LATERAL VIEW. 2) It's not executed eagerly, i.e. `sql("...multi-insert clause...")` won't take place right away like other commands, e.g. CREATE TABLE.
This PR removes these limitations and make us fully support multi-insert.
## How was this patch tested?
new tests in `SQLQuerySuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11754 from cloud-fan/lateral-view.
## What changes were proposed in this pull request?
Follow up to https://github.com/apache/spark/pull/11657
- Also update `String.getBytes("UTF-8")` to use `StandardCharsets.UTF_8`
- And fix one last new Coverity warning that turned up (use of unguarded `wait()` replaced by simpler/more robust `java.util.concurrent` classes in tests)
- And while we're here cleaning up Coverity warnings, just fix about 15 more build warnings
## How was this patch tested?
Jenkins tests
Author: Sean Owen <sowen@cloudera.com>
Closes#11725 from srowen/SPARK-13823.2.
## What changes were proposed in this pull request?
Remove the wrong "expected" parameter in MathFunctionsSuite.scala's checkNaNWithoutCodegen.
This function is to check NaN value, so the "expected" parameter is useless. The Callers do not pass "expected" value and the similar function like checkNaNWithGeneratedProjection and checkNaNWithOptimization do not use it also.
Author: Yucai Yu <yucai.yu@intel.com>
Closes#11718 from yucai/unused_expected.
#### What changes were proposed in this pull request?
Before this PR, two Optimizer rules `ColumnPruning` and `PushPredicateThroughProject` reverse each other's effects. Optimizer always reaches the max iteration when optimizing some queries. Extra `Project` are found in the plan. For example, below is the optimized plan after reaching 100 iterations:
```
Join Inner, Some((cast(id1#16 as bigint) = id1#18L))
:- Project [id1#16]
: +- Filter isnotnull(cast(id1#16 as bigint))
: +- Project [id1#16]
: +- Relation[id1#16,newCol#17] JSON part: struct<>, data: struct<id1:int,newCol:int>
+- Filter isnotnull(id1#18L)
+- Relation[id1#18L] JSON part: struct<>, data: struct<id1:bigint>
```
This PR splits the optimizer rule `ColumnPruning` to `ColumnPruning` and `EliminateOperators`
The issue becomes worse when having another rule `NullFiltering`, which could add extra Filters for `IsNotNull`. We have to be careful when introducing extra `Filter` if the benefit is not large enough. Another PR will be submitted by sameeragarwal to handle this issue.
cc sameeragarwal marmbrus
In addition, `ColumnPruning` should not push `Project` through non-deterministic `Filter`. This could cause wrong results. This will be put in a separate PR.
cc davies cloud-fan yhuai
#### How was this patch tested?
Modified the existing test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#11682 from gatorsmile/viewDuplicateNames.
This PR adds a new strategy, `FileSourceStrategy`, that can be used for planning scans of collections of files that might be partitioned or bucketed.
Compared with the existing planning logic in `DataSourceStrategy` this version has the following desirable properties:
- It removes the need to have `RDD`, `broadcastedHadoopConf` and other distributed concerns in the public API of `org.apache.spark.sql.sources.FileFormat`
- Partition column appending is delegated to the format to avoid an extra copy / devectorization when appending partition columns
- It minimizes the amount of data that is shipped to each executor (i.e. it does not send the whole list of files to every worker in the form of a hadoop conf)
- it natively supports bucketing files into partitions, and thus does not require coalescing / creating a `UnionRDD` with the correct partitioning.
- Small files are automatically coalesced into fewer tasks using an approximate bin-packing algorithm.
Currently only a testing source is planned / tested using this strategy. In follow-up PRs we will port the existing formats to this API.
A stub for `FileScanRDD` is also added, but most methods remain unimplemented.
Other minor cleanups:
- partition pruning is pushed into `FileCatalog` so both the new and old code paths can use this logic. This will also allow future implementations to use indexes or other tricks (i.e. a MySQL metastore)
- The partitions from the `FileCatalog` now propagate information about file sizes all the way up to the planner so we can intelligently spread files out.
- `Array` -> `Seq` in some internal APIs to avoid unnecessary `toArray` calls
- Rename `Partition` to `PartitionDirectory` to differentiate partitions used earlier in pruning from those where we have already enumerated the files and their sizes.
Author: Michael Armbrust <michael@databricks.com>
Closes#11646 from marmbrus/fileStrategy.
## What changes were proposed in this pull request?
This PR fixes 135 typos over 107 files:
* 121 typos in comments
* 11 typos in testcase name
* 3 typos in log messages
## How was this patch tested?
Manual.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11689 from dongjoon-hyun/fix_more_typos.
## What changes were proposed in this pull request?
- Fixes calls to `new String(byte[])` or `String.getBytes()` that rely on platform default encoding, to use UTF-8
- Same for `InputStreamReader` and `OutputStreamWriter` constructors
- Standardizes on UTF-8 everywhere
- Standardizes specifying the encoding with `StandardCharsets.UTF-8`, not the Guava constant or "UTF-8" (which means handling `UnuspportedEncodingException`)
- (also addresses the other remaining Coverity scan issues, which are pretty trivial; these are separated into commit 1deecd8d9c )
## How was this patch tested?
Jenkins tests
Author: Sean Owen <sowen@cloudera.com>
Closes#11657 from srowen/SPARK-13823.
## What changes were proposed in this pull request?
This PR split the PhysicalRDD into two classes, PhysicalRDD and PhysicalScan. PhysicalRDD is used for DataFrames that is created from existing RDD. PhysicalScan is used for DataFrame that is created from data sources. This enable use to apply different optimization on both of them.
Also fix the problem for sameResult() on two DataSourceScan.
Also fix the equality check to toString for `In`. It's better to use Seq there, but we can't break this public API (sad).
## How was this patch tested?
Existing tests. Manually tested with TPCDS query Q59 and Q64, all those duplicated exchanges can be re-used now, also saw there are 40+% performance improvement (saving half of the scan).
Author: Davies Liu <davies@databricks.com>
Closes#11514 from davies/existing_rdd.
## What changes were proposed in this pull request?
This patch is ported over from viirya's changes in #11048. Currently for most DDLs we just pass the query text directly to Hive. Instead, we should parse these commands ourselves and in the future (not part of this patch) use the `HiveCatalog` to process these DDLs. This is a pretext to merging `SQLContext` and `HiveContext`.
Note: As of this patch we still pass the query text to Hive. The difference is that we now parse the commands ourselves so in the future we can just use our own catalog.
## How was this patch tested?
Jenkins, new `DDLCommandSuite`, which comprises of about 40% of the changes here.
Author: Andrew Or <andrew@databricks.com>
Closes#11573 from andrewor14/parser-plus-plus.
## What changes were proposed in this pull request?
Add SQL generation support for window functions. The idea is simple, just treat `Window` operator like `Project`, i.e. add subquery to its child when necessary, generate a `SELECT ... FROM ...` SQL string, implement `sql` method for window related expressions, e.g. `WindowSpecDefinition`, `WindowFrame`, etc.
This PR also fixed SPARK-13720 by improving the process of adding extra `SubqueryAlias`(the `RecoverScopingInfo` rule). Before this PR, we update the qualifiers in project list while adding the subquery. However, this is incomplete as we need to update qualifiers in all ancestors that refer attributes here. In this PR, we split `RecoverScopingInfo` into 2 rules: `AddSubQuery` and `UpdateQualifier`. `AddSubQuery` only add subquery if necessary, and `UpdateQualifier` will re-propagate and update qualifiers bottom up.
Ideally we should put the bug fix part in an individual PR, but this bug also blocks the window stuff, so I put them together here.
Many thanks to gatorsmile for the initial discussion and test cases!
## How was this patch tested?
new tests in `LogicalPlanToSQLSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11555 from cloud-fan/window.
#### What changes were proposed in this pull request?
`projectList` is useless. Its value is always the same as the child.output. Remove it from the class `Window`. Removal can simplify the codes in Analyzer and Optimizer.
This PR is based on the discussion started by cloud-fan in a separate PR:
https://github.com/apache/spark/pull/5604#discussion_r55140466
This PR also eliminates useless `Window`.
cloud-fan yhuai
#### How was this patch tested?
Existing test cases cover it.
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#11565 from gatorsmile/removeProjListWindow.
## What changes were proposed in this pull request?
This PR adds support for inferring an additional set of data constraints based on attribute equality. For e.g., if an operator has constraints of the form (`a = 5`, `a = b`), we can now automatically infer an additional constraint of the form `b = 5`
## How was this patch tested?
Tested that new constraints are properly inferred for filters (by adding a new test) and equi-joins (by modifying an existing test)
Author: Sameer Agarwal <sameer@databricks.com>
Closes#11618 from sameeragarwal/infer-isequal-constraints.
## What changes were proposed in this pull request?
This PR unifies DataFrame and Dataset by migrating existing DataFrame operations to Dataset and make `DataFrame` a type alias of `Dataset[Row]`.
Most Scala code changes are source compatible, but Java API is broken as Java knows nothing about Scala type alias (mostly replacing `DataFrame` with `Dataset<Row>`).
There are several noticeable API changes related to those returning arrays:
1. `collect`/`take`
- Old APIs in class `DataFrame`:
```scala
def collect(): Array[Row]
def take(n: Int): Array[Row]
```
- New APIs in class `Dataset[T]`:
```scala
def collect(): Array[T]
def take(n: Int): Array[T]
def collectRows(): Array[Row]
def takeRows(n: Int): Array[Row]
```
Two specialized methods `collectRows` and `takeRows` are added because Java doesn't support returning generic arrays. Thus, for example, `DataFrame.collect(): Array[T]` actually returns `Object` instead of `Array<T>` from Java side.
Normally, Java users may fall back to `collectAsList` and `takeAsList`. The two new specialized versions are added to avoid performance regression in ML related code (but maybe I'm wrong and they are not necessary here).
1. `randomSplit`
- Old APIs in class `DataFrame`:
```scala
def randomSplit(weights: Array[Double], seed: Long): Array[DataFrame]
def randomSplit(weights: Array[Double]): Array[DataFrame]
```
- New APIs in class `Dataset[T]`:
```scala
def randomSplit(weights: Array[Double], seed: Long): Array[Dataset[T]]
def randomSplit(weights: Array[Double]): Array[Dataset[T]]
```
Similar problem as above, but hasn't been addressed for Java API yet. We can probably add `randomSplitAsList` to fix this one.
1. `groupBy`
Some original `DataFrame.groupBy` methods have conflicting signature with original `Dataset.groupBy` methods. To distinguish these two, typed `Dataset.groupBy` methods are renamed to `groupByKey`.
Other noticeable changes:
1. Dataset always do eager analysis now
We used to support disabling DataFrame eager analysis to help reporting partially analyzed malformed logical plan on analysis failure. However, Dataset encoders requires eager analysi during Dataset construction. To preserve the error reporting feature, `AnalysisException` now takes an extra `Option[LogicalPlan]` argument to hold the partially analyzed plan, so that we can check the plan tree when reporting test failures. This plan is passed by `QueryExecution.assertAnalyzed`.
## How was this patch tested?
Existing tests do the work.
## TODO
- [ ] Fix all tests
- [ ] Re-enable MiMA check
- [ ] Update ScalaDoc (`since`, `group`, and example code)
Author: Cheng Lian <lian@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Author: Wenchen Fan <wenchen@databricks.com>
Author: Cheng Lian <liancheng@users.noreply.github.com>
Closes#11443 from liancheng/ds-to-df.
## What changes were proposed in this pull request?
Since the opening curly brace, '{', has many usages as discussed in [SPARK-3854](https://issues.apache.org/jira/browse/SPARK-3854), this PR adds a ScalaStyle rule to prevent '){' pattern for the following majority pattern and fixes the code accordingly. If we enforce this in ScalaStyle from now, it will improve the Scala code quality and reduce review time.
```
// Correct:
if (true) {
println("Wow!")
}
// Incorrect:
if (true){
println("Wow!")
}
```
IntelliJ also shows new warnings based on this.
## How was this patch tested?
Pass the Jenkins ScalaStyle test.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11637 from dongjoon-hyun/SPARK-3854.
## What changes were proposed in this pull request?
We should reuse an object similar to the other non-primitive type getters. For
a query that computes averages over decimal columns, this shows a 10% speedup
on overall query times.
## How was this patch tested?
Existing tests and this benchmark
```
TPCDS Snappy: Best/Avg Time(ms) Rate(M/s) Per Row(ns)
--------------------------------------------------------------------------------
q27-agg (master) 10627 / 11057 10.8 92.3
q27-agg (this patch) 9722 / 9832 11.8 84.4
```
Author: Nong Li <nong@databricks.com>
Closes#11624 from nongli/spark-13790.
## What changes were proposed in this pull request?
This PR adds support for inferring `IsNotNull` constraints from expressions with an `!==`. More specifically, if an operator has a condition on `a !== b`, we know that both `a` and `b` in the operator output can no longer be null.
## How was this patch tested?
1. Modified a test in `ConstraintPropagationSuite` to test for expressions with an inequality.
2. Added a test in `NullFilteringSuite` for making sure an Inner join with a "non-equal" condition appropriately filters out null from their input.
cc nongli
Author: Sameer Agarwal <sameer@databricks.com>
Closes#11594 from sameeragarwal/isnotequal-constraints.
## What changes were proposed in this pull request?
A very minor change for using `BigDecimal.decimal(f: Float)` instead of `BigDecimal(f: float)`. The latter is deprecated and can result in inconsistencies due to an implicit conversion to `Double`.
## How was this patch tested?
N/A
cc yhuai
Author: Sameer Agarwal <sameer@databricks.com>
Closes#11597 from sameeragarwal/bigdecimal.
## What changes were proposed in this pull request?
This PR is a small follow up on https://github.com/apache/spark/pull/11338 (https://issues.apache.org/jira/browse/SPARK-13092) to use `ExpressionSet` as part of the verification logic in `ConstraintPropagationSuite`.
## How was this patch tested?
No new tests added. Just changes the verification logic in `ConstraintPropagationSuite`.
Author: Sameer Agarwal <sameer@databricks.com>
Closes#11611 from sameeragarwal/expression-set.
#### What changes were proposed in this pull request?
Remove all the deterministic conditions in a [[Filter]] that are contained in the Child's Constraints.
For example, the first query can be simplified to the second one.
```scala
val queryWithUselessFilter = tr1
.where("tr1.a".attr > 10 || "tr1.c".attr < 10)
.join(tr2.where('d.attr < 100), Inner, Some("tr1.a".attr === "tr2.a".attr))
.where(
("tr1.a".attr > 10 || "tr1.c".attr < 10) &&
'd.attr < 100 &&
"tr2.a".attr === "tr1.a".attr)
```
```scala
val query = tr1
.where("tr1.a".attr > 10 || "tr1.c".attr < 10)
.join(tr2.where('d.attr < 100), Inner, Some("tr1.a".attr === "tr2.a".attr))
```
#### How was this patch tested?
Six test cases are added.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#11406 from gatorsmile/FilterRemoval.
## What changes were proposed in this pull request?
It’s possible to have common parts in a query, for example, self join, it will be good to avoid the duplicated part to same CPUs and memory (Broadcast or cache).
Exchange will materialize the underlying RDD by shuffle or collect, it’s a great point to check duplicates and reuse them. Duplicated exchanges means they generate exactly the same result inside a query.
In order to find out the duplicated exchanges, we should be able to compare SparkPlan to check that they have same results or not. We already have that for LogicalPlan, so we should move that into QueryPlan to make it available for SparkPlan.
Once we can find the duplicated exchanges, we should replace all of them with same SparkPlan object (could be wrapped by ReusedExchage for explain), then the plan tree become a DAG. Since all the planner only work with tree, so this rule should be the last one for the entire planning.
After the rule, the plan will looks like:
```
WholeStageCodegen
: +- Project [id#0L]
: +- BroadcastHashJoin [id#0L], [id#2L], Inner, BuildRight, None
: :- Project [id#0L]
: : +- BroadcastHashJoin [id#0L], [id#1L], Inner, BuildRight, None
: : :- Range 0, 1, 4, 1024, [id#0L]
: : +- INPUT
: +- INPUT
:- BroadcastExchange HashedRelationBroadcastMode(true,List(id#1L),List(id#1L))
: +- WholeStageCodegen
: : +- Range 0, 1, 4, 1024, [id#1L]
+- ReusedExchange [id#2L], BroadcastExchange HashedRelationBroadcastMode(true,List(id#1L),List(id#1L))
```
For three ways SortMergeJoin,
```
== Physical Plan ==
WholeStageCodegen
: +- Project [id#0L]
: +- SortMergeJoin [id#0L], [id#4L], None
: :- INPUT
: +- INPUT
:- WholeStageCodegen
: : +- Project [id#0L]
: : +- SortMergeJoin [id#0L], [id#3L], None
: : :- INPUT
: : +- INPUT
: :- WholeStageCodegen
: : : +- Sort [id#0L ASC], false, 0
: : : +- INPUT
: : +- Exchange hashpartitioning(id#0L, 200), None
: : +- WholeStageCodegen
: : : +- Range 0, 1, 4, 33554432, [id#0L]
: +- WholeStageCodegen
: : +- Sort [id#3L ASC], false, 0
: : +- INPUT
: +- ReusedExchange [id#3L], Exchange hashpartitioning(id#0L, 200), None
+- WholeStageCodegen
: +- Sort [id#4L ASC], false, 0
: +- INPUT
+- ReusedExchange [id#4L], Exchange hashpartitioning(id#0L, 200), None
```
If the same ShuffleExchange or BroadcastExchange, execute()/executeBroadcast() will be called by different parents, they should cached the RDD/Broadcast, return the same one for all the parents.
## How was this patch tested?
Added some unit tests for this. Had done some manual tests on TPCDS query Q59 and Q64, we can see some exchanges are re-used (this requires a change in PhysicalRDD to for sameResult, is be done in #11514 ).
Author: Davies Liu <davies@databricks.com>
Closes#11403 from davies/dedup.
#### What changes were proposed in this pull request?
As shown in another PR: https://github.com/apache/spark/pull/11596, we are using `SELECT 1` as a dummy table, when the table is used for SQL statements in which a table reference is required, but the contents of the table are not important. For example,
```SQL
SELECT value FROM (select 1) dummyTable Lateral View explode(array(1,2,3)) adTable as value
```
Before the PR, the optimized plan contains a useless `Project` after Optimizer executing the `ColumnPruning` rule, as shown below:
```
== Analyzed Logical Plan ==
value: int
Project [value#22]
+- Generate explode(array(1, 2, 3)), true, false, Some(adtable), [value#22]
+- SubqueryAlias dummyTable
+- Project [1 AS 1#21]
+- OneRowRelation$
== Optimized Logical Plan ==
Generate explode([1,2,3]), false, false, Some(adtable), [value#22]
+- Project
+- OneRowRelation$
```
After the fix, the optimized plan removed the useless `Project`, as shown below:
```
== Optimized Logical Plan ==
Generate explode([1,2,3]), false, false, Some(adtable), [value#22]
+- OneRowRelation$
```
This PR is to remove `Project` when its Child's output is Nil
#### How was this patch tested?
Added a new unit test case into the suite `ColumnPruningSuite.scala`
Author: gatorsmile <gatorsmile@gmail.com>
Closes#11599 from gatorsmile/projectOneRowRelation.
## What changes were proposed in this pull request?
If there are many branches in a CaseWhen expression, the generated code could go above the 64K limit for single java method, will fail to compile. This PR change it to fallback to interpret mode if there are more than 20 branches.
This PR is based on #11243 and #11221, thanks to joehalliwell
Closes#11243Closes#11221
## How was this patch tested?
Add a test with 50 branches.
Author: Davies Liu <davies@databricks.com>
Closes#11592 from davies/fix_when.
## What changes were proposed in this pull request?
Analysis exception occurs while running the following query.
```
SELECT ints FROM nestedArray LATERAL VIEW explode(a.b) `a` AS `ints`
```
```
Failed to analyze query: org.apache.spark.sql.AnalysisException: cannot resolve '`ints`' given input columns: [a, `ints`]; line 1 pos 7
'Project ['ints]
+- Generate explode(a#0.b), true, false, Some(a), [`ints`#8]
+- SubqueryAlias nestedarray
+- LocalRelation [a#0], [[[[1,2,3]]]]
```
## How was this patch tested?
Added new unit tests in SQLQuerySuite and HiveQlSuite
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#11538 from dilipbiswal/SPARK-13698.
## What changes were proposed in this pull request?
In order to make `docs/examples` (and other related code) more simple/readable/user-friendly, this PR replaces existing codes like the followings by using `diamond` operator.
```
- final ArrayList<Product2<Object, Object>> dataToWrite =
- new ArrayList<Product2<Object, Object>>();
+ final ArrayList<Product2<Object, Object>> dataToWrite = new ArrayList<>();
```
Java 7 or higher supports **diamond** operator which replaces the type arguments required to invoke the constructor of a generic class with an empty set of type parameters (<>). Currently, Spark Java code use mixed usage of this.
## How was this patch tested?
Manual.
Pass the existing tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11541 from dongjoon-hyun/SPARK-13702.
## What changes were proposed in this pull request?
`ScalaReflection.mirror` method should be synchronized when scala version is `2.10` because `universe.runtimeMirror` is not thread safe.
## How was this patch tested?
I added a test to check thread safety of `ScalaRefection.mirror` method in `ScalaReflectionSuite`, which will throw the following Exception in Scala `2.10` without this patch:
```
[info] - thread safety of mirror *** FAILED *** (49 milliseconds)
[info] java.lang.UnsupportedOperationException: tail of empty list
[info] at scala.collection.immutable.Nil$.tail(List.scala:339)
[info] at scala.collection.immutable.Nil$.tail(List.scala:334)
[info] at scala.reflect.internal.SymbolTable.popPhase(SymbolTable.scala:172)
[info] at scala.reflect.internal.Symbols$Symbol.unsafeTypeParams(Symbols.scala:1477)
[info] at scala.reflect.internal.Symbols$TypeSymbol.tpe(Symbols.scala:2777)
[info] at scala.reflect.internal.Mirrors$RootsBase.init(Mirrors.scala:235)
[info] at scala.reflect.runtime.JavaMirrors$class.createMirror(JavaMirrors.scala:34)
[info] at scala.reflect.runtime.JavaMirrors$class.runtimeMirror(JavaMirrors.scala:61)
[info] at scala.reflect.runtime.JavaUniverse.runtimeMirror(JavaUniverse.scala:12)
[info] at scala.reflect.runtime.JavaUniverse.runtimeMirror(JavaUniverse.scala:12)
[info] at org.apache.spark.sql.catalyst.ScalaReflection$.mirror(ScalaReflection.scala:36)
[info] at org.apache.spark.sql.catalyst.ScalaReflectionSuite$$anonfun$12$$anonfun$apply$mcV$sp$1$$anonfun$apply$1$$anonfun$apply$2.apply(ScalaReflectionSuite.scala:256)
[info] at org.apache.spark.sql.catalyst.ScalaReflectionSuite$$anonfun$12$$anonfun$apply$mcV$sp$1$$anonfun$apply$1$$anonfun$apply$2.apply(ScalaReflectionSuite.scala:252)
[info] at scala.concurrent.impl.Future$PromiseCompletingRunnable.liftedTree1$1(Future.scala:24)
[info] at scala.concurrent.impl.Future$PromiseCompletingRunnable.run(Future.scala:24)
[info] at scala.concurrent.impl.ExecutionContextImpl$$anon$3.exec(ExecutionContextImpl.scala:107)
[info] at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
[info] at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
[info] at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
[info] at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
```
Notice that the test will pass when Scala version is `2.11`.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#11487 from ueshin/issues/SPARK-13640.
This PR replaces #9925 which had issues with CI. **Please see the original PR for any previous discussions.**
## What changes were proposed in this pull request?
Deprecate the SparkSQL column operator !== and use =!= as an alternative.
Fixes subtle issues related to operator precedence (basically, !== does not have the same priority as its logical negation, ===).
## How was this patch tested?
All currently existing tests.
Author: Jakob Odersky <jodersky@gmail.com>
Closes#11588 from jodersky/SPARK-7286.
## What changes were proposed in this pull request?
If a filter predicate or a join condition consists of `IsNotNull` checks, we should reorder these checks such that these non-nullability checks are evaluated before the rest of the predicates.
For e.g., if a filter predicate is of the form `a > 5 && isNotNull(b)`, we should rewrite this as `isNotNull(b) && a > 5` during physical plan generation.
## How was this patch tested?
new unit tests that verify the physical plan for both filters and joins in `ReorderedPredicateSuite`
Author: Sameer Agarwal <sameer@databricks.com>
Closes#11511 from sameeragarwal/reorder-isnotnull.
## What changes were proposed in this pull request?
This removes the remaining deprecated Octal escape literals. The followings are the warnings on those two lines.
```
LiteralExpressionSuite.scala:99: Octal escape literals are deprecated, use \u0000 instead.
HiveQlSuite.scala:74: Octal escape literals are deprecated, use \u002c instead.
```
## How was this patch tested?
Manual.
During building, there should be no warning on `Octal escape literals`.
```
mvn -DskipTests clean install
```
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11584 from dongjoon-hyun/SPARK-13400.
## What changes were proposed in this pull request?
This PR add SQL generation support for aggregate with multi-distinct, by simply moving the `DistinctAggregationRewriter` rule to optimizer.
More discussions are needed as this breaks an import contract: analyzed plan should be able to run without optimization. However, the `ComputeCurrentTime` rule has kind of broken it already, and I think maybe we should add a new phase for this kind of rules, because strictly speaking they don't belong to analysis and is coupled with the physical plan implementation.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11579 from cloud-fan/distinct.
## What changes were proposed in this pull request?
In order to avoid StackOverflow when parse a expression with hundreds of ORs, we should use loop instead of recursive functions to flatten the tree as list. This PR also build a balanced tree to reduce the depth of generated And/Or expression, to avoid StackOverflow in analyzer/optimizer.
## How was this patch tested?
Add new unit tests. Manually tested with TPCDS Q3 with hundreds predicates in it [1]. These predicates help to reduce the number of partitions, then the query time went from 60 seconds to 8 seconds.
[1] https://github.com/cloudera/impala-tpcds-kit/blob/master/queries/q3.sql
Author: Davies Liu <davies@databricks.com>
Closes#11501 from davies/long_or.
## What changes were proposed in this pull request?
The code in `Expand.apply` can be simplified by existing information:
* the `groupByExprs` parameter are all `Attribute`s
* the `child` parameter is a `Project` that append aliased group by expressions to its child's output
## How was this patch tested?
by existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11485 from cloud-fan/expand.
## What changes were proposed in this pull request?
This PR change the way how we generate the code for the output variables passing from a plan to it's parent.
Right now, they are generated before call consume() of it's parent. It's not efficient, if the parent is a Filter or Join, which could filter out most the rows, the time to access some of the columns that are not used by the Filter or Join are wasted.
This PR try to improve this by defering the access of columns until they are actually used by a plan. After this PR, a plan does not need to generate code to evaluate the variables for output, just passing the ExprCode to its parent by `consume()`. In `parent.consumeChild()`, it will check the output from child and `usedInputs`, generate the code for those columns that is part of `usedInputs` before calling `doConsume()`.
This PR also change the `if` from
```
if (cond) {
xxx
}
```
to
```
if (!cond) continue;
xxx
```
The new one could help to reduce the nested indents for multiple levels of Filter and BroadcastHashJoin.
It also added some comments for operators.
## How was the this patch tested?
Unit tests. Manually ran TPCDS Q55, this PR improve the performance about 30% (scale=10, from 2.56s to 1.96s)
Author: Davies Liu <davies@databricks.com>
Closes#11274 from davies/gen_defer.
## What changes were proposed in this pull request?
When we add more DDL parsing logic in the future, SparkQl will become very big. To keep it smaller, we'll introduce helper "parser objects", e.g. one to parse alter table commands. However, these parser objects will need to access some helper methods that exist in CatalystQl. The proposal is to move those methods to an isolated ParserUtils object.
This is based on viirya's changes in #11048. It prefaces the bigger fix for SPARK-13139 to make the diff of that patch smaller.
## How was this patch tested?
No change in functionality, so just Jenkins.
Author: Andrew Or <andrew@databricks.com>
Closes#11529 from andrewor14/parser-utils.
`HadoopFsRelation` is used for reading most files into Spark SQL. However today this class mixes the concerns of file management, schema reconciliation, scan building, bucketing, partitioning, and writing data. As a result, many data sources are forced to reimplement the same functionality and the various layers have accumulated a fair bit of inefficiency. This PR is a first cut at separating this into several components / interfaces that are each described below. Additionally, all implementations inside of Spark (parquet, csv, json, text, orc, svmlib) have been ported to the new API `FileFormat`. External libraries, such as spark-avro will also need to be ported to work with Spark 2.0.
### HadoopFsRelation
A simple `case class` that acts as a container for all of the metadata required to read from a datasource. All discovery, resolution and merging logic for schemas and partitions has been removed. This an internal representation that no longer needs to be exposed to developers.
```scala
case class HadoopFsRelation(
sqlContext: SQLContext,
location: FileCatalog,
partitionSchema: StructType,
dataSchema: StructType,
bucketSpec: Option[BucketSpec],
fileFormat: FileFormat,
options: Map[String, String]) extends BaseRelation
```
### FileFormat
The primary interface that will be implemented by each different format including external libraries. Implementors are responsible for reading a given format and converting it into `InternalRow` as well as writing out an `InternalRow`. A format can optionally return a schema that is inferred from a set of files.
```scala
trait FileFormat {
def inferSchema(
sqlContext: SQLContext,
options: Map[String, String],
files: Seq[FileStatus]): Option[StructType]
def prepareWrite(
sqlContext: SQLContext,
job: Job,
options: Map[String, String],
dataSchema: StructType): OutputWriterFactory
def buildInternalScan(
sqlContext: SQLContext,
dataSchema: StructType,
requiredColumns: Array[String],
filters: Array[Filter],
bucketSet: Option[BitSet],
inputFiles: Array[FileStatus],
broadcastedConf: Broadcast[SerializableConfiguration],
options: Map[String, String]): RDD[InternalRow]
}
```
The current interface is based on what was required to get all the tests passing again, but still mixes a couple of concerns (i.e. `bucketSet` is passed down to the scan instead of being resolved by the planner). Additionally, scans are still returning `RDD`s instead of iterators for single files. In a future PR, bucketing should be removed from this interface and the scan should be isolated to a single file.
### FileCatalog
This interface is used to list the files that make up a given relation, as well as handle directory based partitioning.
```scala
trait FileCatalog {
def paths: Seq[Path]
def partitionSpec(schema: Option[StructType]): PartitionSpec
def allFiles(): Seq[FileStatus]
def getStatus(path: Path): Array[FileStatus]
def refresh(): Unit
}
```
Currently there are two implementations:
- `HDFSFileCatalog` - based on code from the old `HadoopFsRelation`. Infers partitioning by recursive listing and caches this data for performance
- `HiveFileCatalog` - based on the above, but it uses the partition spec from the Hive Metastore.
### ResolvedDataSource
Produces a logical plan given the following description of a Data Source (which can come from DataFrameReader or a metastore):
- `paths: Seq[String] = Nil`
- `userSpecifiedSchema: Option[StructType] = None`
- `partitionColumns: Array[String] = Array.empty`
- `bucketSpec: Option[BucketSpec] = None`
- `provider: String`
- `options: Map[String, String]`
This class is responsible for deciding which of the Data Source APIs a given provider is using (including the non-file based ones). All reconciliation of partitions, buckets, schema from metastores or inference is done here.
### DataSourceAnalysis / DataSourceStrategy
Responsible for analyzing and planning reading/writing of data using any of the Data Source APIs, including:
- pruning the files from partitions that will be read based on filters.
- appending partition columns*
- applying additional filters when a data source can not evaluate them internally.
- constructing an RDD that is bucketed correctly when required*
- sanity checking schema match-up and other analysis when writing.
*In the future we should do that following:
- Break out file handling into its own Strategy as its sufficiently complex / isolated.
- Push the appending of partition columns down in to `FileFormat` to avoid an extra copy / unvectorization.
- Use a custom RDD for scans instead of `SQLNewNewHadoopRDD2`
Author: Michael Armbrust <michael@databricks.com>
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11509 from marmbrus/fileDataSource.
#### What changes were proposed in this pull request?
Non-deterministic predicates should not be pushed through Generate.
#### How was this patch tested?
Added a test case in `FilterPushdownSuite.scala`
Author: gatorsmile <gatorsmile@gmail.com>
Closes#11562 from gatorsmile/pushPredicateDownWindow.
## What changes were proposed in this pull request?
This PR adds an optimizer rule to eliminate reading (unnecessary) NULL values if they are not required for correctness by inserting `isNotNull` filters is the query plan. These filters are currently inserted beneath existing `Filter` and `Join` operators and are inferred based on their data constraints.
Note: While this optimization is applicable to all types of join, it primarily benefits `Inner` and `LeftSemi` joins.
## How was this patch tested?
1. Added a new `NullFilteringSuite` that tests for `IsNotNull` filters in the query plan for joins and filters. Also, tests interaction with the `CombineFilters` optimizer rules.
2. Test generated ExpressionTrees via `OrcFilterSuite`
3. Test filter source pushdown logic via `SimpleTextHadoopFsRelationSuite`
cc yhuai nongli
Author: Sameer Agarwal <sameer@databricks.com>
Closes#11372 from sameeragarwal/gen-isnotnull.
## What changes were proposed in this pull request?
It's weird that expressions don't always have all the expressions in it. This PR marks `QueryPlan.expressions` final to forbid sub classes overriding it to exclude some expressions. Currently only `Generate` override it, we can use `producedAttributes` to fix the unresolved attribute problem for it.
Note that this PR doesn't fix the problem in #11497
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11532 from cloud-fan/generate.
## What changes were proposed in this pull request?
```
Seq(("id1", "value1")).toDF("key", "value").registerTempTable("src")
sqlContext.sql("SELECT t1.* FROM src LATERAL VIEW explode(map('key1', 100, 'key2', 200)) t1 AS key, value")
```
Results in following logical plan
```
Project [key#2,value#3]
+- Generate explode(HiveGenericUDF#org.apache.hadoop.hive.ql.udf.generic.GenericUDFMap(key1,100,key2,200)), true, false, Some(genoutput), [key#2,value#3]
+- SubqueryAlias src
+- Project [_1#0 AS key#2,_2#1 AS value#3]
+- LocalRelation [_1#0,_2#1], [[id1,value1]]
```
The above query fails with following runtime error.
```
java.lang.ClassCastException: java.lang.Integer cannot be cast to org.apache.spark.unsafe.types.UTF8String
at org.apache.spark.sql.catalyst.expressions.BaseGenericInternalRow$class.getUTF8String(rows.scala:46)
at org.apache.spark.sql.catalyst.expressions.GenericInternalRow.getUTF8String(rows.scala:221)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(generated.java:42)
at org.apache.spark.sql.execution.Generate$$anonfun$doExecute$1$$anonfun$apply$9.apply(Generate.scala:98)
at org.apache.spark.sql.execution.Generate$$anonfun$doExecute$1$$anonfun$apply$9.apply(Generate.scala:96)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:370)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:370)
at scala.collection.Iterator$class.foreach(Iterator.scala:742)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1194)
<stack-trace omitted.....>
```
In this case the generated outputs are wrongly resolved from its child (LocalRelation) due to
https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala#L537-L548
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
Added unit tests in hive/SQLQuerySuite and AnalysisSuite
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#11497 from dilipbiswal/spark-13651.
## What changes were proposed in this pull request?
Today we have `analysis.Catalog` and `catalog.Catalog`. In the future the former will call the latter. When that happens, if both of them are still called `Catalog` it will be very confusing. This patch renames the latter `ExternalCatalog` because it is expected to talk to external systems.
## How was this patch tested?
Jenkins.
Author: Andrew Or <andrew@databricks.com>
Closes#11526 from andrewor14/rename-catalog.
#### What changes were proposed in this pull request?
This PR is for supporting SQL generation for cube, rollup and grouping sets.
For example, a query using rollup:
```SQL
SELECT count(*) as cnt, key % 5, grouping_id() FROM t1 GROUP BY key % 5 WITH ROLLUP
```
Original logical plan:
```
Aggregate [(key#17L % cast(5 as bigint))#47L,grouping__id#46],
[(count(1),mode=Complete,isDistinct=false) AS cnt#43L,
(key#17L % cast(5 as bigint))#47L AS _c1#45L,
grouping__id#46 AS _c2#44]
+- Expand [List(key#17L, value#18, (key#17L % cast(5 as bigint))#47L, 0),
List(key#17L, value#18, null, 1)],
[key#17L,value#18,(key#17L % cast(5 as bigint))#47L,grouping__id#46]
+- Project [key#17L,
value#18,
(key#17L % cast(5 as bigint)) AS (key#17L % cast(5 as bigint))#47L]
+- Subquery t1
+- Relation[key#17L,value#18] ParquetRelation
```
Converted SQL:
```SQL
SELECT count( 1) AS `cnt`,
(`t1`.`key` % CAST(5 AS BIGINT)),
grouping_id() AS `_c2`
FROM `default`.`t1`
GROUP BY (`t1`.`key` % CAST(5 AS BIGINT))
GROUPING SETS (((`t1`.`key` % CAST(5 AS BIGINT))), ())
```
#### How was the this patch tested?
Added eight test cases in `LogicalPlanToSQLSuite`.
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#11283 from gatorsmile/groupingSetsToSQL.
## What changes were proposed in this pull request?
This patch simply moves things to existing package `o.a.s.sql.catalyst.parser` in an effort to reduce the size of the diff in #11048. This is conceptually the same as a recently merged patch #11482.
## How was this patch tested?
Jenkins.
Author: Andrew Or <andrew@databricks.com>
Closes#11506 from andrewor14/parser-package.
## What changes were proposed in this pull request?
This is support SQL generation for subquery expressions, which will be replaced to a SubqueryHolder inside SQLBuilder recursively.
## How was this patch tested?
Added unit tests.
Author: Davies Liu <davies@databricks.com>
Closes#11453 from davies/sql_subquery.
## What changes were proposed in this pull request?
This PR support visualization for subquery in SQL web UI, also improve the explain of subquery, especially when it's used together with whole stage codegen.
For example:
```python
>>> sqlContext.range(100).registerTempTable("range")
>>> sqlContext.sql("select id / (select sum(id) from range) from range where id > (select id from range limit 1)").explain(True)
== Parsed Logical Plan ==
'Project [unresolvedalias(('id / subquery#9), None)]
: +- 'SubqueryAlias subquery#9
: +- 'Project [unresolvedalias('sum('id), None)]
: +- 'UnresolvedRelation `range`, None
+- 'Filter ('id > subquery#8)
: +- 'SubqueryAlias subquery#8
: +- 'GlobalLimit 1
: +- 'LocalLimit 1
: +- 'Project [unresolvedalias('id, None)]
: +- 'UnresolvedRelation `range`, None
+- 'UnresolvedRelation `range`, None
== Analyzed Logical Plan ==
(id / scalarsubquery()): double
Project [(cast(id#0L as double) / cast(subquery#9 as double)) AS (id / scalarsubquery())#11]
: +- SubqueryAlias subquery#9
: +- Aggregate [(sum(id#0L),mode=Complete,isDistinct=false) AS sum(id)#10L]
: +- SubqueryAlias range
: +- Range 0, 100, 1, 4, [id#0L]
+- Filter (id#0L > subquery#8)
: +- SubqueryAlias subquery#8
: +- GlobalLimit 1
: +- LocalLimit 1
: +- Project [id#0L]
: +- SubqueryAlias range
: +- Range 0, 100, 1, 4, [id#0L]
+- SubqueryAlias range
+- Range 0, 100, 1, 4, [id#0L]
== Optimized Logical Plan ==
Project [(cast(id#0L as double) / cast(subquery#9 as double)) AS (id / scalarsubquery())#11]
: +- SubqueryAlias subquery#9
: +- Aggregate [(sum(id#0L),mode=Complete,isDistinct=false) AS sum(id)#10L]
: +- Range 0, 100, 1, 4, [id#0L]
+- Filter (id#0L > subquery#8)
: +- SubqueryAlias subquery#8
: +- GlobalLimit 1
: +- LocalLimit 1
: +- Project [id#0L]
: +- Range 0, 100, 1, 4, [id#0L]
+- Range 0, 100, 1, 4, [id#0L]
== Physical Plan ==
WholeStageCodegen
: +- Project [(cast(id#0L as double) / cast(subquery#9 as double)) AS (id / scalarsubquery())#11]
: : +- Subquery subquery#9
: : +- WholeStageCodegen
: : : +- TungstenAggregate(key=[], functions=[(sum(id#0L),mode=Final,isDistinct=false)], output=[sum(id)#10L])
: : : +- INPUT
: : +- Exchange SinglePartition, None
: : +- WholeStageCodegen
: : : +- TungstenAggregate(key=[], functions=[(sum(id#0L),mode=Partial,isDistinct=false)], output=[sum#14L])
: : : +- Range 0, 1, 4, 100, [id#0L]
: +- Filter (id#0L > subquery#8)
: : +- Subquery subquery#8
: : +- CollectLimit 1
: : +- WholeStageCodegen
: : : +- Project [id#0L]
: : : +- Range 0, 1, 4, 100, [id#0L]
: +- Range 0, 1, 4, 100, [id#0L]
```
The web UI looks like:
This PR also change the tree structure of WholeStageCodegen to make it consistent than others. Before this change, Both WholeStageCodegen and InputAdapter hold a references to the same plans, those could be updated without notify another, causing problems, this is discovered by #11403 .
## How was this patch tested?
Existing tests, also manual tests with the example query, check the explain and web UI.
Author: Davies Liu <davies@databricks.com>
Closes#11417 from davies/viz_subquery.
## What changes were proposed in this pull request?
After SPARK-6990, `dev/lint-java` keeps Java code healthy and helps PR review by saving much time.
This issue aims remove unused imports from Java/Scala code and add `UnusedImports` checkstyle rule to help developers.
## How was this patch tested?
```
./dev/lint-java
./build/sbt compile
```
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11438 from dongjoon-hyun/SPARK-13583.
## What changes were proposed in this pull request?
Make some cross-cutting code improvements according to static analysis. These are individually up for discussion since they exist in separate commits that can be reverted. The changes are broadly:
- Inner class should be static
- Mismatched hashCode/equals
- Overflow in compareTo
- Unchecked warnings
- Misuse of assert, vs junit.assert
- get(a) + getOrElse(b) -> getOrElse(a,b)
- Array/String .size -> .length (occasionally, -> .isEmpty / .nonEmpty) to avoid implicit conversions
- Dead code
- tailrec
- exists(_ == ) -> contains find + nonEmpty -> exists filter + size -> count
- reduce(_+_) -> sum map + flatten -> map
The most controversial may be .size -> .length simply because of its size. It is intended to avoid implicits that might be expensive in some places.
## How was the this patch tested?
Existing Jenkins unit tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#11292 from srowen/SPARK-13423.
## What changes were proposed in this pull request?
#11479 [SPARK-13627] broke 2.10 compatibility: [2.10-Build](https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Compile/job/spark-master-compile-maven-scala-2.10/292/console)
At this moment, we need to support both 2.10 and 2.11.
This PR recovers some deprecated methods which were replace by [SPARK-13627].
## How was this patch tested?
Jenkins build: Both 2.10, 2.11.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11488 from dongjoon-hyun/hotfix_compatibility_with_2.10.
JIRA: https://issues.apache.org/jira/browse/SPARK-13466
## What changes were proposed in this pull request?
With column pruning rule in optimizer, some Project operators will become redundant. We should remove these redundant Projects.
For an example query:
val input = LocalRelation('key.int, 'value.string)
val query =
Project(Seq($"x.key", $"y.key"),
Join(
SubqueryAlias("x", input),
BroadcastHint(SubqueryAlias("y", input)), Inner, None))
After the first run of column pruning, it would like:
Project(Seq($"x.key", $"y.key"),
Join(
Project(Seq($"x.key"), SubqueryAlias("x", input)),
Project(Seq($"y.key"), <-- inserted by the rule
BroadcastHint(SubqueryAlias("y", input))),
Inner, None))
Actually we don't need the outside Project now. This patch will remove it:
Join(
Project(Seq($"x.key"), SubqueryAlias("x", input)),
Project(Seq($"y.key"),
BroadcastHint(SubqueryAlias("y", input))),
Inner, None)
## How was the this patch tested?
Unit test is added into ColumnPruningSuite.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#11341 from viirya/remove-redundant-project.
JIRA: https://issues.apache.org/jira/browse/SPARK-13635
## What changes were proposed in this pull request?
LimitPushdown optimizer rule has been disabled due to no whole-stage codegen for Limit. As we have whole-stage codegen for Limit now, we should enable it.
## How was this patch tested?
As we only re-enable LimitPushdown optimizer rule, no need to add new tests for it.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#11483 from viirya/enable-limitpushdown.
## What changes were proposed in this pull request?
This PR aims to fix the following deprecation warnings.
* MethodSymbolApi.paramss--> paramLists
* AnnotationApi.tpe -> tree.tpe
* BufferLike.readOnly -> toList.
* StandardNames.nme -> termNames
* scala.tools.nsc.interpreter.AbstractFileClassLoader -> scala.reflect.internal.util.AbstractFileClassLoader
* TypeApi.declarations-> decls
## How was this patch tested?
Check the compile build log and pass the tests.
```
./build/sbt
```
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11479 from dongjoon-hyun/SPARK-13627.
## What changes were proposed in this pull request?
The `trait GroupingAnalytics` only has one implementation, it's an unnecessary abstraction. This PR removes it, and does some code simplification when resolving `GroupingSet`.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11469 from cloud-fan/groupingset.
#### What changes were proposed in this pull request?
This PR is to prune unnecessary columns when the operator is `MapPartitions`. The solution is to add an extra `Project` in the child node.
For the other two operators `AppendColumns` and `MapGroups`, it sounds doable. More discussions are required. The major reason is the current implementation of the `inputPlan` of `groupBy` is based on the child of `AppendColumns`. It might be a bug? Thus, will submit a separate PR.
#### How was this patch tested?
Added a test case in ColumnPruningSuite to verify the rule. Added another test case in DatasetSuite.scala to verify the data.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#11460 from gatorsmile/datasetPruningNew.
## What changes were proposed in this pull request?
Change in class FormatNumber to make it work irrespective of locale.
## How was this patch tested?
Unit tests.
Author: lgieron <lgieron@gmail.com>
Closes#11396 from lgieron/SPARK-13515_Fix_Format_Number.
## What changes were proposed in this pull request?
This PR defer the resolution from a id of dictionary to value until the column is actually accessed (inside getInt/getLong), this is very useful for those columns and rows that are filtered out. It's also useful for binary type, we will not need to copy all the byte arrays.
This PR also change the underlying type for small decimal that could be fit within a Int, in order to use getInt() to lookup the value from IntDictionary.
## How was this patch tested?
Manually test TPCDS Q7 with scale factor 10, saw about 30% improvements (after PR #11274).
Author: Davies Liu <davies@databricks.com>
Closes#11437 from davies/decode_dict.
## What changes were proposed in this pull request?
This PR adds support for implementing whole state codegen for sort. Builds heaving on nongli 's PR: https://github.com/apache/spark/pull/11008 (which actually implements the feature), and adds the following changes on top:
- [x] Generated code updates peak execution memory metrics
- [x] Unit tests in `WholeStageCodegenSuite` and `SQLMetricsSuite`
## How was this patch tested?
New unit tests in `WholeStageCodegenSuite` and `SQLMetricsSuite`. Further, all existing sort tests should pass.
Author: Sameer Agarwal <sameer@databricks.com>
Author: Nong Li <nong@databricks.com>
Closes#11359 from sameeragarwal/sort-codegen.
#### What changes were proposed in this pull request?
After analysis by Analyzer, two operators could have alias. They are `Project` and `Aggregate`. So far, we only rewrite and propagate constraints if `Alias` is defined in `Project`. This PR is to resolve this issue in `Aggregate`.
#### How was this patch tested?
Added a test case for `Aggregate` in `ConstraintPropagationSuite`.
marmbrus sameeragarwal
Author: gatorsmile <gatorsmile@gmail.com>
Closes#11422 from gatorsmile/validConstraintsInUnaryNodes.
## What changes were proposed in this pull request?
Nested classes defined within Scala objects are translated into Java static nested classes. Unlike inner classes, they don't need outer scopes. But the analyzer still thinks that an outer scope is required.
This PR fixes this issue simply by checking whether a nested class is static before looking up its outer scope.
## How was this patch tested?
A test case is added to `DatasetSuite`. It checks contents of a Dataset whose element type is a nested class declared in a Scala object.
Author: Cheng Lian <lian@databricks.com>
Closes#11421 from liancheng/spark-13540-object-as-outer-scope.
## What changes were proposed in this pull request?
Currently, BroadcastNestedLoopJoin is implemented for worst case, it's too slow, very easy to hang forever. This PR will create fast path for some joinType and buildSide, also improve the worst case (will use much less memory than before).
Before this PR, one task requires O(N*K) + O(K) in worst cases, N is number of rows from one partition of streamed table, it could hang the job (because of GC).
In order to workaround this for InnerJoin, we have to disable auto-broadcast, switch to CartesianProduct: This could be workaround for InnerJoin, see https://forums.databricks.com/questions/6747/how-do-i-get-a-cartesian-product-of-a-huge-dataset.html
In this PR, we will have fast path for these joins :
InnerJoin with BuildLeft or BuildRight
LeftOuterJoin with BuildRight
RightOuterJoin with BuildLeft
LeftSemi with BuildRight
These fast paths are all stream based (take one pass on streamed table), required O(1) memory.
All other join types and build types will take two pass on streamed table, one pass to find the matched rows that includes streamed part, which require O(1) memory, another pass to find the rows from build table that does not have a matched row from streamed table, which required O(K) memory, K is the number rows from build side, one bit per row, should be much smaller than the memory for broadcast. The following join types work in this way:
LeftOuterJoin with BuildLeft
RightOuterJoin with BuildRight
FullOuterJoin with BuildLeft or BuildRight
LeftSemi with BuildLeft
This PR also added tests for all the join types for BroadcastNestedLoopJoin.
After this PR, for InnerJoin with one small table, BroadcastNestedLoopJoin should be faster than CartesianProduct, we don't need that workaround anymore.
## How was the this patch tested?
Added unit tests.
Author: Davies Liu <davies@databricks.com>
Closes#11328 from davies/nested_loop.
## What changes were proposed in this pull request?
Predicates shouldn't be pushed through project with nondeterministic field(s).
See https://github.com/graphframes/graphframes/pull/23 and SPARK-13473 for more details.
This PR targets master, branch-1.6, and branch-1.5.
## How was this patch tested?
A test case is added in `FilterPushdownSuite`. It constructs a query plan where a filter is over a project with a nondeterministic field. Optimized query plan shouldn't change in this case.
Author: Cheng Lian <lian@databricks.com>
Closes#11348 from liancheng/spark-13473-no-ppd-through-nondeterministic-project-field.
## What changes were proposed in this pull request?
This PR mostly rewrite the ColumnPruning rule to support most of the SQL logical plans (except those for Dataset).
This PR also fix a bug in Generate, it should always output UnsafeRow, added an regression test for that.
## How was this patch tested?
This is test by unit tests, also manually test with TPCDS Q78, which could prune all unused columns successfully, improved the performance by 78% (from 22s to 12s).
Author: Davies Liu <davies@databricks.com>
Closes#11354 from davies/fix_column_pruning.
This PR adds a new abstraction called an `ExpressionSet` which attempts to canonicalize expressions to remove cosmetic differences. Deterministic expressions that are in the set after canonicalization will always return the same answer given the same input (i.e. false positives should not be possible). However, it is possible that two canonical expressions that are not equal will in fact return the same answer given any input (i.e. false negatives are possible).
```scala
val set = AttributeSet('a + 1 :: 1 + 'a :: Nil)
set.iterator => Iterator('a + 1)
set.contains('a + 1) => true
set.contains(1 + 'a) => true
set.contains('a + 2) => false
```
Other relevant changes include:
- Since this concept overlaps with the existing `semanticEquals` and `semanticHash`, those functions are also ported to this new infrastructure.
- A memoized `canonicalized` version of the expression is added as a `lazy val` to `Expression` and is used by both `semanticEquals` and `ExpressionSet`.
- A set of unit tests for `ExpressionSet` are added
- Tests which expect `semanticEquals` to be less intelligent than it now is are updated.
As a followup, we should consider auditing the places where we do `O(n)` `semanticEquals` operations and replace them with `ExpressionSet`. We should also consider consolidating `AttributeSet` as a specialized factory for an `ExpressionSet.`
Author: Michael Armbrust <michael@databricks.com>
Closes#11338 from marmbrus/expressionSet.
## What changes were proposed in this pull request?
Reverting SPARK-13376 (d563c8fa01) affects the test added by SPARK-13383. So, I am fixing the test.
Author: Yin Huai <yhuai@databricks.com>
Closes#11355 from yhuai/SPARK-13383-fix-test.
JIRA: https://issues.apache.org/jira/browse/SPARK-13383
## What changes were proposed in this pull request?
When we do column pruning in Optimizer, we put additional Project on top of a logical plan. However, when we already wrap a BroadcastHint on a logical plan, the added Project will hide BroadcastHint after later execution.
We should take care of BroadcastHint when we do column pruning.
## How was the this patch tested?
Unit test is added.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#11260 from viirya/keep-broadcasthint.
## What changes were proposed in this pull request?
This PR pull all the keywords (and some others) from ExpressionParser.g as KeywordParser.g, because ExpressionParser is too large to compile.
## How was the this patch tested?
unit test, maven build
Closes#11329
Author: Davies Liu <davies@databricks.com>
Closes#11331 from davies/split_expr.
## What changes were proposed in this pull request?
This PR mostly rewrite the ColumnPruning rule to support most of the SQL logical plans (except those for Dataset).
## How was the this patch tested?
This is test by unit tests, also manually test with TPCDS Q78, which could prune all unused columns successfully, improved the performance by 78% (from 22s to 12s).
Author: Davies Liu <davies@databricks.com>
Closes#11256 from davies/fix_column_pruning.
The current implementation of statistics of UnaryNode does not considering output (for example, Project may product much less columns than it's child), we should considering it to have a better guess.
We usually only join with few columns from a parquet table, the size of projected plan could be much smaller than the original parquet files. Having a better guess of size help we choose between broadcast join or sort merge join.
After this PR, I saw a few queries choose broadcast join other than sort merge join without turning spark.sql.autoBroadcastJoinThreshold for every query, ended up with about 6-8X improvements on end-to-end time.
We use `defaultSize` of DataType to estimate the size of a column, currently For DecimalType/StringType/BinaryType and UDT, we are over-estimate too much (4096 Bytes), so this PR change them to some more reasonable values. Here are the new defaultSize for them:
DecimalType: 8 or 16 bytes, based on the precision
StringType: 20 bytes
BinaryType: 100 bytes
UDF: default size of SQL type
These numbers are not perfect (hard to have a perfect number for them), but should be better than 4096.
Author: Davies Liu <davies@databricks.com>
Closes#11210 from davies/statics.
The type checking functions of `If` and `UnwrapOption` are fixed to eliminate spurious failures. `UnwrapOption` was checking for an input of `ObjectType` but `ObjectType`'s accept function was hard coded to return `false`. `If`'s type check was returning a false negative in the case that the two options differed only by nullability.
Tests added:
- an end-to-end regression test is added to `DatasetSuite` for the reported failure.
- all the unit tests in `ExpressionEncoderSuite` are augmented to also confirm successful analysis. These tests are actually what pointed out the additional issues with `If` resolution.
Author: Michael Armbrust <michael@databricks.com>
Closes#11316 from marmbrus/datasetOptions.
#### What changes were proposed in this pull request?
Ensure that all built-in expressions can be mapped to its SQL representation if there is one (e.g. ScalaUDF doesn't have a SQL representation). The function lists are from the expression list in `FunctionRegistry`.
window functions, grouping sets functions (`cube`, `rollup`, `grouping`, `grouping_id`), generator functions (`explode` and `json_tuple`) are covered by separate JIRA and PRs. Thus, this PR does not cover them. Except these functions, all the built-in expressions are covered. For details, see the list in `ExpressionToSQLSuite`.
Fixed a few issues. For example, the `prettyName` of `approx_count_distinct` is not right. The `sql` of `hash` function is not right, since the `hash` function does not accept `seed`.
Additionally, also correct the order of expressions in `FunctionRegistry` so that people are easier to find which functions are missing.
cc liancheng
#### How was the this patch tested?
Added two test cases in LogicalPlanToSQLSuite for covering `not like` and `not in`.
Added a new test suite `ExpressionToSQLSuite` to cover the functions:
1. misc non-aggregate functions + complex type creators + null expressions
2. math functions
3. aggregate functions
4. string functions
5. date time functions + calendar interval
6. collection functions
7. misc functions
Author: gatorsmile <gatorsmile@gmail.com>
Closes#11314 from gatorsmile/expressionToSQL.
## What changes were proposed in this pull request?
This PR tries to fix all typos in all markdown files under `docs` module,
and fixes similar typos in other comments, too.
## How was the this patch tested?
manual tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11300 from dongjoon-hyun/minor_fix_typos.
JIRA: https://issues.apache.org/jira/browse/SPARK-13321
The following SQL can not be parsed with current parser:
SELECT `u_1`.`id` FROM (((SELECT `t0`.`id` FROM `default`.`t0`) UNION ALL (SELECT `t0`.`id` FROM `default`.`t0`)) UNION ALL (SELECT `t0`.`id` FROM `default`.`t0`)) AS u_1
We should fix it.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#11204 from viirya/nested-union.
## What changes were proposed in this pull request?
This PR adds equality operators to UDT classes so that they can be correctly tested for dataType equality during union operations.
This was previously causing `"AnalysisException: u"unresolved operator 'Union;""` when trying to unionAll two dataframes with UDT columns as below.
```
from pyspark.sql.tests import PythonOnlyPoint, PythonOnlyUDT
from pyspark.sql import types
schema = types.StructType([types.StructField("point", PythonOnlyUDT(), True)])
a = sqlCtx.createDataFrame([[PythonOnlyPoint(1.0, 2.0)]], schema)
b = sqlCtx.createDataFrame([[PythonOnlyPoint(3.0, 4.0)]], schema)
c = a.unionAll(b)
```
## How was the this patch tested?
Tested using two unit tests in sql/test.py and the DataFrameSuite.
Additional information here : https://issues.apache.org/jira/browse/SPARK-13410
Author: Franklyn D'souza <franklynd@gmail.com>
Closes#11279 from damnMeddlingKid/udt-union-all.
## What changes were proposed in this pull request?
This is a step towards merging `SQLContext` and `HiveContext`. A new internal Catalog API was introduced in #10982 and extended in #11069. This patch introduces an implementation of this API using `HiveClient`, an existing interface to Hive. It also extends `HiveClient` with additional calls to Hive that are needed to complete the catalog implementation.
*Where should I start reviewing?* The new catalog introduced is `HiveCatalog`. This class is relatively simple because it just calls `HiveClientImpl`, where most of the new logic is. I would not start with `HiveClient`, `HiveQl`, or `HiveMetastoreCatalog`, which are modified mainly because of a refactor.
*Why is this patch so big?* I had to refactor HiveClient to remove an intermediate representation of databases, tables, partitions etc. After this refactor `CatalogTable` convert directly to and from `HiveTable` (etc.). Otherwise we would have to first convert `CatalogTable` to the intermediate representation and then convert that to HiveTable, which is messy.
The new class hierarchy is as follows:
```
org.apache.spark.sql.catalyst.catalog.Catalog
- org.apache.spark.sql.catalyst.catalog.InMemoryCatalog
- org.apache.spark.sql.hive.HiveCatalog
```
Note that, as of this patch, none of these classes are currently used anywhere yet. This will come in the future before the Spark 2.0 release.
## How was the this patch tested?
All existing unit tests, and HiveCatalogSuite that extends CatalogTestCases.
Author: Andrew Or <andrew@databricks.com>
Author: Reynold Xin <rxin@databricks.com>
Closes#11293 from rxin/hive-catalog.
Quite a few Spark SQL join operators broadcast one side of the join to all nodes. The are a few problems with this:
- This conflates broadcasting (a data exchange) with joining. Data exchanges should be managed by a different operator.
- All these nodes implement their own (duplicate) broadcasting logic.
- Re-use of indices is quite hard.
This PR defines both a ```BroadcastDistribution``` and ```BroadcastPartitioning```, these contain a `BroadcastMode`. The `BroadcastMode` defines the way in which we transform the Array of `InternalRow`'s into an index. We currently support the following `BroadcastMode`'s:
- IdentityBroadcastMode: This broadcasts the rows in their original form.
- HashSetBroadcastMode: This applies a projection to the input rows, deduplicates these rows and broadcasts the resulting `Set`.
- HashedRelationBroadcastMode: This transforms the input rows into a `HashedRelation`, and broadcasts this index.
To match this distribution we implement a ```BroadcastExchange``` operator which will perform the broadcast for us, and have ```EnsureRequirements``` plan this operator. The old Exchange operator has been renamed into ShuffleExchange in order to clearly separate between Shuffled and Broadcasted exchanges. Finally the classes in Exchange.scala have been moved to a dedicated package.
cc rxin davies
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#11083 from hvanhovell/SPARK-13136.
## What changes were proposed in this pull request?
This pull request fixes some minor issues (documentation, test flakiness, test organization) with #11190, which was merged earlier tonight.
## How was the this patch tested?
unit tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#11285 from rxin/subquery.
## What changes were proposed in this pull request?
This patch renames logical.Subquery to logical.SubqueryAlias, which is a more appropriate name for this operator (versus subqueries as expressions).
## How was the this patch tested?
Unit tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#11288 from rxin/SPARK-13420.
This PR introduces several major changes:
1. Replacing `Expression.prettyString` with `Expression.sql`
The `prettyString` method is mostly an internal, developer faced facility for debugging purposes, and shouldn't be exposed to users.
1. Using SQL-like representation as column names for selected fields that are not named expression (back-ticks and double quotes should be removed)
Before, we were using `prettyString` as column names when possible, and sometimes the result column names can be weird. Here are several examples:
Expression | `prettyString` | `sql` | Note
------------------ | -------------- | ---------- | ---------------
`a && b` | `a && b` | `a AND b` |
`a.getField("f")` | `a[f]` | `a.f` | `a` is a struct
1. Adding trait `NonSQLExpression` extending from `Expression` for expressions that don't have a SQL representation (e.g. Scala UDF/UDAF and Java/Scala object expressions used for encoders)
`NonSQLExpression.sql` may return an arbitrary user facing string representation of the expression.
Author: Cheng Lian <lian@databricks.com>
Closes#10757 from liancheng/spark-12799.simplify-expression-string-methods.
```scala
// case 1: missing sort columns are resolvable if join is true
sql("SELECT explode(a) AS val, b FROM data WHERE b < 2 order by val, c")
// case 2: missing sort columns are not resolvable if join is false. Thus, issue an error message in this case
sql("SELECT explode(a) AS val FROM data order by val, c")
```
When sort columns are not in `Generate`, we can resolve them when `join` is equal to `true`. Still trying to add more test cases for the other `UnaryNode` types.
Could you review the changes? davies cloud-fan Thanks!
Author: gatorsmile <gatorsmile@gmail.com>
Closes#11198 from gatorsmile/missingInSort.
Conversion of outer joins, if the predicates in filter conditions can restrict the result sets so that all null-supplying rows are eliminated.
- `full outer` -> `inner` if both sides have such predicates
- `left outer` -> `inner` if the right side has such predicates
- `right outer` -> `inner` if the left side has such predicates
- `full outer` -> `left outer` if only the left side has such predicates
- `full outer` -> `right outer` if only the right side has such predicates
If applicable, this can greatly improve the performance, since outer join is much slower than inner join, full outer join is much slower than left/right outer join.
The original PR is https://github.com/apache/spark/pull/10542
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#10567 from gatorsmile/outerJoinEliminationByFilterCond.
This PR adds support for rewriting constraints if there are aliases in the query plan. For e.g., if there is a query of form `SELECT a, a AS b`, any constraints on `a` now also apply to `b`.
JIRA: https://issues.apache.org/jira/browse/SPARK-13091
cc marmbrus
Author: Sameer Agarwal <sameer@databricks.com>
Closes#11144 from sameeragarwal/alias.
JIRA: https://issues.apache.org/jira/browse/SPARK-13384
## What changes were proposed in this pull request?
When we de-duplicate attributes in Analyzer, we create new attributes. However, we don't keep original qualifiers. Some plans will be failed to analysed. We should keep original qualifiers in new attributes.
## How was the this patch tested?
Unit test is added.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#11261 from viirya/keep-attr-qualifiers.
`rand` and `randn` functions with a `seed` argument are commonly used. Based on the common sense, the results of `rand` and `randn` should be deterministic if the `seed` parameter value is provided. For example, in MS SQL Server, it also has a function `rand`. Regarding the parameter `seed`, the description is like: ```Seed is an integer expression (tinyint, smallint, or int) that gives the seed value. If seed is not specified, the SQL Server Database Engine assigns a seed value at random. For a specified seed value, the result returned is always the same.```
Update: the current implementation is unable to generate deterministic results when the partitions are not fixed. This PR documents this issue in the function descriptions.
jkbradley hit an issue and provided an example in the following JIRA: https://issues.apache.org/jira/browse/SPARK-13333
Author: gatorsmile <gatorsmile@gmail.com>
Closes#11232 from gatorsmile/randSeed.
Currently, the columns in projects of Expand that are not used by Aggregate are not pruned, this PR fix that.
Author: Davies Liu <davies@databricks.com>
Closes#11225 from davies/fix_pruning_expand.
Add `LazilyGenerateOrdering` to support generated ordering for `RangePartitioner` of `Exchange` instead of `InterpretedOrdering`.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#10894 from ueshin/issues/SPARK-12976.
Using GroupingSets will generate a wrong result when Aggregate Functions containing GroupBy columns.
This PR is to fix it. Since the code changes are very small. Maybe we also can merge it to 1.6
For example, the following query returns a wrong result:
```scala
sql("select course, sum(earnings) as sum from courseSales group by course, earnings" +
" grouping sets((), (course), (course, earnings))" +
" order by course, sum").show()
```
Before the fix, the results are like
```
[null,null]
[Java,null]
[Java,20000.0]
[Java,30000.0]
[dotNET,null]
[dotNET,5000.0]
[dotNET,10000.0]
[dotNET,48000.0]
```
After the fix, the results become correct:
```
[null,113000.0]
[Java,20000.0]
[Java,30000.0]
[Java,50000.0]
[dotNET,5000.0]
[dotNET,10000.0]
[dotNET,48000.0]
[dotNET,63000.0]
```
UPDATE: This PR also deprecated the external column: GROUPING__ID.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#11100 from gatorsmile/groupingSets.
This patch adds a new optimizer rule for performing limit pushdown. Limits will now be pushed down in two cases:
- If a limit is on top of a `UNION ALL` operator, then a partition-local limit operator will be pushed to each of the union operator's children.
- If a limit is on top of an `OUTER JOIN` then a partition-local limit will be pushed to one side of the join. For `LEFT OUTER` and `RIGHT OUTER` joins, the limit will be pushed to the left and right side, respectively. For `FULL OUTER` join, we will only push limits when at most one of the inputs is already limited: if one input is limited we will push a smaller limit on top of it and if neither input is limited then we will limit the input which is estimated to be larger.
These optimizations were proposed previously by gatorsmile in #10451 and #10454, but those earlier PRs were closed and deferred for later because at that time Spark's physical `Limit` operator would trigger a full shuffle to perform global limits so there was a chance that pushdowns could actually harm performance by causing additional shuffles/stages. In #7334, we split the `Limit` operator into separate `LocalLimit` and `GlobalLimit` operators, so we can now push down only local limits (which don't require extra shuffles). This patch is based on both of gatorsmile's patches, with changes and simplifications due to partition-local-limiting.
When we push down the limit, we still keep the original limit in place, so we need a mechanism to ensure that the optimizer rule doesn't keep pattern-matching once the limit has been pushed down. In order to handle this, this patch adds a `maxRows` method to `SparkPlan` which returns the maximum number of rows that the plan can compute, then defines the pushdown rules to only push limits to children if the children's maxRows are greater than the limit's maxRows. This idea is carried over from #10451; see that patch for additional discussion.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#11121 from JoshRosen/limit-pushdown-2.
The java `Calendar` object is expensive to create. I have a sub query like this `SELECT a, b, c FROM table UV WHERE (datediff(UV.visitDate, '1997-01-01')>=0 AND datediff(UV.visitDate, '2015-01-01')<=0))`
The table stores `visitDate` as String type and has 3 billion records. A `Calendar` object is created every time `DateTimeUtils.stringToDate` is called. By reusing the `Calendar` object, I saw about 20 seconds performance improvement for this stage.
Author: Carson Wang <carson.wang@intel.com>
Closes#11090 from carsonwang/SPARK-13185.
The current implementation of ResolveSortReferences can only push one missing attributes into it's child, it failed to analyze TPCDS Q98, because of there are two missing attributes in that (one from Window, another from Aggregate).
Author: Davies Liu <davies@databricks.com>
Closes#11153 from davies/resolve_sort.
JIRA: https://issues.apache.org/jira/browse/SPARK-13277
There is an ANTLR warning during compilation:
warning(200): org/apache/spark/sql/catalyst/parser/SparkSqlParser.g:938:7:
Decision can match input such as "KW_USING Identifier" using multiple alternatives: 2, 3
As a result, alternative(s) 3 were disabled for that input
This patch is to fix it.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#11168 from viirya/fix-parser-using.
The parser currently parses the following strings without a hitch:
* Table Identifier:
* `a.b.c` should fail, but results in the following table identifier `a.b`
* `table!#` should fail, but results in the following table identifier `table`
* Expression
* `1+2 r+e` should fail, but results in the following expression `1 + 2`
This PR fixes this by adding terminated rules for both expression parsing and table identifier parsing.
cc cloud-fan (we discussed this in https://github.com/apache/spark/pull/10649) jayadevanmurali (this causes your PR https://github.com/apache/spark/pull/11051 to fail)
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#11159 from hvanhovell/SPARK-13276.
Grouping() returns a column is aggregated or not, grouping_id() returns the aggregation levels.
grouping()/grouping_id() could be used with window function, but does not work in having/sort clause, will be fixed by another PR.
The GROUPING__ID/grouping_id() in Hive is wrong (according to docs), we also did it wrongly, this PR change that to match the behavior in most databases (also the docs of Hive).
Author: Davies Liu <davies@databricks.com>
Closes#10677 from davies/grouping.
Some analysis rules generate aliases or auxiliary attribute references with the same name but different expression IDs. For example, `ResolveAggregateFunctions` introduces `havingCondition` and `aggOrder`, and `DistinctAggregationRewriter` introduces `gid`.
This is OK for normal query execution since these attribute references get expression IDs. However, it's troublesome when converting resolved query plans back to SQL query strings since expression IDs are erased.
Here's an example Spark 1.6.0 snippet for illustration:
```scala
sqlContext.range(10).select('id as 'a, 'id as 'b).registerTempTable("t")
sqlContext.sql("SELECT SUM(a) FROM t GROUP BY a, b ORDER BY COUNT(a), COUNT(b)").explain(true)
```
The above code produces the following resolved plan:
```
== Analyzed Logical Plan ==
_c0: bigint
Project [_c0#101L]
+- Sort [aggOrder#102L ASC,aggOrder#103L ASC], true
+- Aggregate [a#47L,b#48L], [(sum(a#47L),mode=Complete,isDistinct=false) AS _c0#101L,(count(a#47L),mode=Complete,isDistinct=false) AS aggOrder#102L,(count(b#48L),mode=Complete,isDistinct=false) AS aggOrder#103L]
+- Subquery t
+- Project [id#46L AS a#47L,id#46L AS b#48L]
+- LogicalRDD [id#46L], MapPartitionsRDD[44] at range at <console>:26
```
Here we can see that both aggregate expressions in `ORDER BY` are extracted into an `Aggregate` operator, and both of them are named `aggOrder` with different expression IDs.
The solution is to automatically add the expression IDs into the attribute name for the Alias and AttributeReferences that are generated by Analyzer in SQL Generation.
In this PR, it also resolves another issue. Users could use the same name as the internally generated names. The duplicate names should not cause name ambiguity. When resolving the column, Catalyst should not pick the column that is internally generated.
Could you review the solution? marmbrus liancheng
I did not set the newly added flag for all the alias and attribute reference generated by Analyzers. Please let me know if I should do it? Thank you!
Author: gatorsmile <gatorsmile@gmail.com>
Closes#11050 from gatorsmile/namingConflicts.
This PR improve the lookup of BytesToBytesMap by:
1. Generate code for calculate the hash code of grouping keys.
2. Do not use MemoryLocation, fetch the baseObject and offset for key and value directly (remove the indirection).
Author: Davies Liu <davies@databricks.com>
Closes#11010 from davies/gen_map.
Adds the benchmark results as comments.
The codegen version is slower than the interpreted version for `simple` case becasue of 3 reasons:
1. codegen version use a more complex hash algorithm than interpreted version, i.e. `Murmur3_x86_32.hashInt` vs [simple multiplication and addition](https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala#L153).
2. codegen version will write the hash value to a row first and then read it out. I tried to create a `GenerateHasher` that can generate code to return hash value directly and got about 60% speed up for the `simple` case, does it worth?
3. the row in `simple` case only has one int field, so the runtime reflection may be removed because of branch prediction, which makes the interpreted version faster.
The `array` case is also slow for similar reasons, e.g. array elements are of same type, so interpreted version can probably get rid of runtime reflection by branch prediction.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10917 from cloud-fan/hash-benchmark.
nullability should only be considered as an optimization rather than part of the type system, so instead of failing analysis for mismatch nullability, we should pass analysis and add runtime null check.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11035 from cloud-fan/ignore-nullability.
This patch changes the implementation of the physical `Limit` operator so that it relies on the `Exchange` operator to perform data movement rather than directly using `ShuffledRDD`. In addition to improving efficiency, this lays the necessary groundwork for further optimization of limit, such as limit pushdown or whole-stage codegen.
At a high-level, this replaces the old physical `Limit` operator with two new operators, `LocalLimit` and `GlobalLimit`. `LocalLimit` performs per-partition limits, while `GlobalLimit` applies the final limit to a single partition; `GlobalLimit`'s declares that its `requiredInputDistribution` is `SinglePartition`, which will cause the planner to use an `Exchange` to perform the appropriate shuffles. Thus, a logical `Limit` appearing in the middle of a query plan will be expanded into `LocalLimit -> Exchange to one partition -> GlobalLimit`.
In the old code, calling `someDataFrame.limit(100).collect()` or `someDataFrame.take(100)` would actually skip the shuffle and use a fast-path which used `executeTake()` in order to avoid computing all partitions in case only a small number of rows were requested. This patch preserves this optimization by treating logical `Limit` operators specially when they appear as the terminal operator in a query plan: if a `Limit` is the final operator, then we will plan a special `CollectLimit` physical operator which implements the old `take()`-based logic.
In order to be able to match on operators only at the root of the query plan, this patch introduces a special `ReturnAnswer` logical operator which functions similar to `BroadcastHint`: this dummy operator is inserted at the root of the optimized logical plan before invoking the physical planner, allowing the planner to pattern-match on it.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#7334 from JoshRosen/remove-copy-in-limit.
Trivial search-and-replace to eliminate deprecation warnings in Scala 2.11.
Also works with 2.10
Author: Jakob Odersky <jakob@odersky.com>
Closes#11085 from jodersky/SPARK-13171.
https://issues.apache.org/jira/browse/SPARK-12939
Now we will catch `ObjectOperator` in `Analyzer` and resolve the `fromRowExpression/deserializer` inside it. Also update the `MapGroups` and `CoGroup` to pass in `dataAttributes`, so that we can correctly resolve value deserializer(the `child.output` contains both groupking key and values, which may mess things up if they have same-name attribtues). End-to-end tests are added.
follow-ups:
* remove encoders from typed aggregate expression.
* completely remove resolve/bind in `ExpressionEncoder`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10852 from cloud-fan/bug.
This patch incorporates review feedback from #11069, which is already merged.
Author: Andrew Or <andrew@databricks.com>
Closes#11080 from andrewor14/catalog-follow-ups.
Spark SQL should collapse adjacent `Repartition` operators and only keep the last one.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#11064 from JoshRosen/collapse-repartition.
This is a small addendum to #10762 to make the code more robust again future changes.
Author: Reynold Xin <rxin@databricks.com>
Closes#11070 from rxin/SPARK-12828-natural-join.
This is a step towards consolidating `SQLContext` and `HiveContext`.
This patch extends the existing Catalog API added in #10982 to include methods for handling table partitions. In particular, a partition is identified by `PartitionSpec`, which is just a `Map[String, String]`. The Catalog is still not used by anything yet, but its API is now more or less complete and an implementation is fully tested.
About 200 lines are test code.
Author: Andrew Or <andrew@databricks.com>
Closes#11069 from andrewor14/catalog.
The ```SparkSqlLexer``` currently swallows characters which have not been defined in the grammar. This causes problems with SQL commands, such as: ```add jar file:///tmp/ab/TestUDTF.jar```. In this example the `````` is swallowed.
This PR adds an extra Lexer rule to handle such input, and makes a tiny modification to the ```ASTNode```.
cc davies liancheng
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#11052 from hvanhovell/SPARK-13157.
Based on the semantics of a query, we can derive a number of data constraints on output of each (logical or physical) operator. For instance, if a filter defines `‘a > 10`, we know that the output data of this filter satisfies 2 constraints:
1. `‘a > 10`
2. `isNotNull(‘a)`
This PR proposes a possible way of keeping track of these constraints and propagating them in the logical plan, which can then help us build more advanced optimizations (such as pruning redundant filters, optimizing joins, among others). We define constraints as a set of (implicitly conjunctive) expressions. For e.g., if a filter operator has constraints = `Set(‘a > 10, ‘b < 100)`, it’s implied that the outputs satisfy both individual constraints (i.e., `‘a > 10` AND `‘b < 100`).
Design Document: https://docs.google.com/a/databricks.com/document/d/1WQRgDurUBV9Y6CWOBS75PQIqJwT-6WftVa18xzm7nCo/edit?usp=sharing
Author: Sameer Agarwal <sameer@databricks.com>
Closes#10844 from sameeragarwal/constraints.
1. try to avoid the suffix (unique id)
2. remove the comment if there is no code generated.
3. re-arrange the order of functions
4. trop the new line for inlined blocks.
Author: Davies Liu <davies@databricks.com>
Closes#11032 from davies/better_suffix.
when we generate map, we first randomly pick a length, then create a seq of key value pair with the expected length, and finally call `toMap`. However, `toMap` will remove all duplicated keys, which makes the actual map size much less than we expected.
This PR fixes this problem by put keys in a set first, to guarantee we have enough keys to build a map with expected length.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10930 from cloud-fan/random-generator.
The example will throw error like
<console>:20: error: not found: value StructType
Need to add this line:
import org.apache.spark.sql.types._
Author: Kevin (Sangwoo) Kim <sangwookim.me@gmail.com>
Closes#10141 from swkimme/patch-1.
As benchmarked and discussed here: https://github.com/apache/spark/pull/10786/files#r50038294, benefits from codegen, the declarative aggregate function could be much faster than imperative one.
Author: Davies Liu <davies@databricks.com>
Closes#10960 from davies/stddev.
Jira:
https://issues.apache.org/jira/browse/SPARK-13056
Create a map like
{ "a": "somestring", "b": null}
Query like
SELECT col["b"] FROM t1;
NPE would be thrown.
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#10964 from adrian-wang/npewriter.
This pull request creates an internal catalog API. The creation of this API is the first step towards consolidating SQLContext and HiveContext. I envision we will have two different implementations in Spark 2.0: (1) a simple in-memory implementation, and (2) an implementation based on the current HiveClient (ClientWrapper).
I took a look at what Hive's internal metastore interface/implementation, and then created this API based on it. I believe this is the minimal set needed in order to achieve all the needed functionality.
Author: Reynold Xin <rxin@databricks.com>
Closes#10982 from rxin/SPARK-13078.
This includes: float, boolean, short, decimal and calendar interval.
Decimal is mapped to long or byte array depending on the size and calendar
interval is mapped to a struct of int and long.
The only remaining type is map. The schema mapping is straightforward but
we might want to revisit how we deal with this in the rest of the execution
engine.
Author: Nong Li <nong@databricks.com>
Closes#10961 from nongli/spark-13043.
JIRA: https://issues.apache.org/jira/browse/SPARK-12705
**Scope:**
This PR is a general fix for sorting reference resolution when the child's `outputSet` does not have the order-by attributes (called, *missing attributes*):
- UnaryNode support is limited to `Project`, `Window`, `Aggregate`, `Distinct`, `Filter`, `RepartitionByExpression`.
- We will not try to resolve the missing references inside a subquery, unless the outputSet of this subquery contains it.
**General Reference Resolution Rules:**
- Jump over the nodes with the following types: `Distinct`, `Filter`, `RepartitionByExpression`. Do not need to add missing attributes. The reason is their `outputSet` is decided by their `inputSet`, which is the `outputSet` of their children.
- Group-by expressions in `Aggregate`: missing order-by attributes are not allowed to be added into group-by expressions since it will change the query result. Thus, in RDBMS, it is not allowed.
- Aggregate expressions in `Aggregate`: if the group-by expressions in `Aggregate` contains the missing attributes but aggregate expressions do not have it, just add them into the aggregate expressions. This can resolve the analysisExceptions thrown by the three TCPDS queries.
- `Project` and `Window` are special. We just need to add the missing attributes to their `projectList`.
**Implementation:**
1. Traverse the whole tree in a pre-order manner to find all the resolvable missing order-by attributes.
2. Traverse the whole tree in a post-order manner to add the found missing order-by attributes to the node if their `inputSet` contains the attributes.
3. If the origins of the missing order-by attributes are different nodes, each pass only resolves the missing attributes that are from the same node.
**Risk:**
Low. This rule will be trigger iff ```!s.resolved && child.resolved``` is true. Thus, very few cases are affected.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#10678 from gatorsmile/sortWindows.
JIRA: https://issues.apache.org/jira/browse/SPARK-12989
In the rule `ExtractWindowExpressions`, we simply replace alias by the corresponding attribute. However, this will cause an issue exposed by the following case:
```scala
val data = Seq(("a", "b", "c", 3), ("c", "b", "a", 3)).toDF("A", "B", "C", "num")
.withColumn("Data", struct("A", "B", "C"))
.drop("A")
.drop("B")
.drop("C")
val winSpec = Window.partitionBy("Data.A", "Data.B").orderBy($"num".desc)
data.select($"*", max("num").over(winSpec) as "max").explain(true)
```
In this case, both `Data.A` and `Data.B` are `alias` in `WindowSpecDefinition`. If we replace these alias expression by their alias names, we are unable to know what they are since they will not be put in `missingExpr` too.
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#10963 from gatorsmile/seletStarAfterColDrop.
The current implementation is sub-optimal:
* If an expression is always nullable, e.g. `Unhex`, we can still remove null check for children if they are not nullable.
* If an expression has some non-nullable children, we can still remove null check for these children and keep null check for others.
This PR improves this by making the null check elimination more fine-grained.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10987 from cloud-fan/null-check.
JIRA: https://issues.apache.org/jira/browse/SPARK-12689
DDLParser processes three commands: createTable, describeTable and refreshTable.
This patch migrates the three commands to newly absorbed parser.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#10723 from viirya/migrate-ddl-describe.
Make sure we throw better error messages when Parquet schema merging fails.
Author: Cheng Lian <lian@databricks.com>
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#10979 from viirya/schema-merging-failure-message.
In jdk1.7 TimeZone.getTimeZone() is synchronized, so use an instance variable to hold an GMT TimeZone object instead of instantiate it every time.
Author: wangyang <wangyang@haizhi.com>
Closes#10994 from wangyang1992/datetimeUtil.
This patch changes Spark's build to make Scala 2.11 the default Scala version. To be clear, this does not mean that Spark will stop supporting Scala 2.10: users will still be able to compile Spark for Scala 2.10 by following the instructions on the "Building Spark" page; however, it does mean that Scala 2.11 will be the default Scala version used by our CI builds (including pull request builds).
The Scala 2.11 compiler is faster than 2.10, so I think we'll be able to look forward to a slight speedup in our CI builds (it looks like it's about 2X faster for the Maven compile-only builds, for instance).
After this patch is merged, I'll update Jenkins to add new compile-only jobs to ensure that Scala 2.10 compilation doesn't break.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#10608 from JoshRosen/SPARK-6363.
This class is only used for serialization of Python DataFrame. However, we don't require internal row there, so `GenericRowWithSchema` can also do the job.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10992 from cloud-fan/python.
This PR add support for grouping keys for generated TungstenAggregate.
Spilling and performance improvements for BytesToBytesMap will be done by followup PR.
Author: Davies Liu <davies@databricks.com>
Closes#10855 from davies/gen_keys.
Our current Intersect physical operator simply delegates to RDD.intersect. We should remove the Intersect physical operator and simply transform a logical intersect into a semi-join with distinct. This way, we can take advantage of all the benefits of join implementations (e.g. managed memory, code generation, broadcast joins).
After a search, I found one of the mainstream RDBMS did the same. In their query explain, Intersect is replaced by Left-semi Join. Left-semi Join could help outer-join elimination in Optimizer, as shown in the PR: https://github.com/apache/spark/pull/10566
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#10630 from gatorsmile/IntersectBySemiJoin.
1. enable whole stage codegen during tests even there is only one operator supports that.
2. split doProduce() into two APIs: upstream() and doProduce()
3. generate prefix for fresh names of each operator
4. pass UnsafeRow to parent directly (avoid getters and create UnsafeRow again)
5. fix bugs and tests.
This PR re-open #10944 and fix the bug.
Author: Davies Liu <davies@databricks.com>
Closes#10977 from davies/gen_refactor.
A simple workaround to avoid getting parameter types when convert a
logical plan to json.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10970 from cloud-fan/reflection.
JIRA: https://issues.apache.org/jira/browse/SPARK-12968
Implement command to set current database.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#10916 from viirya/ddl-use-database.
JIRA: https://issues.apache.org/jira/browse/SPARK-11955
Currently we simply skip pushdowning filters in parquet if we enable schema merging.
However, we can actually mark particular fields in merging schema for safely pushdowning filters in parquet.
Author: Liang-Chi Hsieh <viirya@appier.com>
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#9940 from viirya/safe-pushdown-parquet-filters.
1. enable whole stage codegen during tests even there is only one operator supports that.
2. split doProduce() into two APIs: upstream() and doProduce()
3. generate prefix for fresh names of each operator
4. pass UnsafeRow to parent directly (avoid getters and create UnsafeRow again)
5. fix bugs and tests.
Author: Davies Liu <davies@databricks.com>
Closes#10944 from davies/gen_refactor.
This PR moves all the functionality provided by the SparkSQLParser/ExtendedHiveQlParser to the new Parser hierarchy (SparkQl/HiveQl). This also improves the current SET command parsing: the current implementation swallows ```set role ...``` and ```set autocommit ...``` commands, this PR respects these commands (and passes them on to Hive).
This PR and https://github.com/apache/spark/pull/10723 end the use of Parser-Combinator parsers for SQL parsing. As a result we can also remove the ```AbstractSQLParser``` in Catalyst.
The PR is marked WIP as long as it doesn't pass all tests.
cc rxin viirya winningsix (this touches https://github.com/apache/spark/pull/10144)
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#10905 from hvanhovell/SPARK-12866.
The error message is now changed from "Do not support type class scala.Tuple2." to "Do not support type class org.json4s.JsonAST$JNull$" to be more informative about what is not supported. Also, StructType metadata now handles JNull correctly, i.e., {'a': None}. test_metadata_null is added to tests.py to show the fix works.
Author: Jason Lee <cjlee@us.ibm.com>
Closes#8969 from jasoncl/SPARK-10847.
This patch adds support for complex types for ColumnarBatch. ColumnarBatch supports structs
and arrays. There is a simple mapping between the richer catalyst types to these two. Strings
are treated as an array of bytes.
ColumnarBatch will contain a column for each node of the schema. Non-complex schemas consists
of just leaf nodes. Structs represent an internal node with one child for each field. Arrays
are internal nodes with one child. Structs just contain nullability. Arrays contain offsets
and lengths into the child array. This structure is able to handle arbitrary nesting. It has
the key property that we maintain columnar throughout and that primitive types are only stored
in the leaf nodes and contiguous across rows. For example, if the schema is
```
array<array<int>>
```
There are three columns in the schema. The internal nodes each have one children. The leaf node contains all the int data stored consecutively.
As part of this, this patch adds append APIs in addition to the Put APIs (e.g. putLong(rowid, v)
vs appendLong(v)). These APIs are necessary when the batch contains variable length elements.
The vectors are not fixed length and will grow as necessary. This should make the usage a lot
simpler for the writer.
Author: Nong Li <nong@databricks.com>
Closes#10820 from nongli/spark-12854.
Otherwise the `^` character is always marked as error in IntelliJ since it represents an unclosed superscript markup tag.
Author: Cheng Lian <lian@databricks.com>
Closes#10926 from liancheng/agg-doc-fix.
As we begin to use unsafe row writing framework(`BufferHolder` and `UnsafeRowWriter`) in more and more places(`UnsafeProjection`, `UnsafeRowParquetRecordReader`, `GenerateColumnAccessor`, etc.), we should add more doc to it and make it easier to use.
This PR abstract the technique used in `UnsafeRowParquetRecordReader`: avoid unnecessary operatition as more as possible. For example, do not always point the row to the buffer at the end, we only need to update the size of row. If all fields are of primitive type, we can even save the row size updating. Then we can apply this technique to more places easily.
a local benchmark shows `UnsafeProjection` is up to 1.7x faster after this PR:
**old version**
```
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
unsafe projection: Avg Time(ms) Avg Rate(M/s) Relative Rate
-------------------------------------------------------------------------------
single long 2616.04 102.61 1.00 X
single nullable long 3032.54 88.52 0.86 X
primitive types 9121.05 29.43 0.29 X
nullable primitive types 12410.60 21.63 0.21 X
```
**new version**
```
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
unsafe projection: Avg Time(ms) Avg Rate(M/s) Relative Rate
-------------------------------------------------------------------------------
single long 1533.34 175.07 1.00 X
single nullable long 2306.73 116.37 0.66 X
primitive types 8403.93 31.94 0.18 X
nullable primitive types 12448.39 21.56 0.12 X
```
For single non-nullable long(the best case), we can have about 1.7x speed up. Even it's nullable, we can still have 1.3x speed up. For other cases, it's not such a boost as the saved operations only take a little proportion of the whole process. The benchmark code is included in this PR.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10809 from cloud-fan/unsafe-projection.
This pull request implements strength reduction for comparing integral expressions and decimal literals, which is more common now because we switch to parsing fractional literals as decimal types (rather than doubles). I added the rules to the existing DecimalPrecision rule with some refactoring to simplify the control flow. I also moved DecimalPrecision rule into its own file due to the growing size.
Author: Reynold Xin <rxin@databricks.com>
Closes#10882 from rxin/SPARK-12904-1.
The current parser turns a decimal literal, for example ```12.1```, into a Double. The problem with this approach is that we convert an exact literal into a non-exact ```Double```. The PR changes this behavior, a Decimal literal is now converted into an extact ```BigDecimal```.
The behavior for scientific decimals, for example ```12.1e01```, is unchanged. This will be converted into a Double.
This PR replaces the ```BigDecimal``` literal by a ```Double``` literal, because the ```BigDecimal``` is the default now. You can use the double literal by appending a 'D' to the value, for instance: ```3.141527D```
cc davies rxin
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#10796 from hvanhovell/SPARK-12848.
Benchmark it on 4 different schemas, the result:
```
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
Hash For simple: Avg Time(ms) Avg Rate(M/s) Relative Rate
-------------------------------------------------------------------------------
interpreted version 31.47 266.54 1.00 X
codegen version 64.52 130.01 0.49 X
```
```
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
Hash For normal: Avg Time(ms) Avg Rate(M/s) Relative Rate
-------------------------------------------------------------------------------
interpreted version 4068.11 0.26 1.00 X
codegen version 1175.92 0.89 3.46 X
```
```
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
Hash For array: Avg Time(ms) Avg Rate(M/s) Relative Rate
-------------------------------------------------------------------------------
interpreted version 9276.70 0.06 1.00 X
codegen version 14762.23 0.04 0.63 X
```
```
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
Hash For map: Avg Time(ms) Avg Rate(M/s) Relative Rate
-------------------------------------------------------------------------------
interpreted version 58869.79 0.01 1.00 X
codegen version 9285.36 0.06 6.34 X
```
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10816 from cloud-fan/hash-benchmark.
The existing `Union` logical operator only supports two children. Thus, adding a new logical operator `Unions` which can have arbitrary number of children to replace the existing one.
`Union` logical plan is a binary node. However, a typical use case for union is to union a very large number of input sources (DataFrames, RDDs, or files). It is not uncommon to union hundreds of thousands of files. In this case, our optimizer can become very slow due to the large number of logical unions. We should change the Union logical plan to support an arbitrary number of children, and add a single rule in the optimizer to collapse all adjacent `Unions` into a single `Unions`. Note that this problem doesn't exist in physical plan, because the physical `Unions` already supports arbitrary number of children.
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#10577 from gatorsmile/unionAllMultiChildren.
Also updated documentation to explain why ComputeCurrentTime and EliminateSubQueries are in the optimizer rather than analyzer.
Author: Reynold Xin <rxin@databricks.com>
Closes#10837 from rxin/optimizer-analyzer-comment.
The three optimization cases are:
1. If the first branch's condition is a true literal, remove the CaseWhen and use the value from that branch.
2. If a branch's condition is a false or null literal, remove that branch.
3. If only the else branch is left, remove the CaseWhen and use the value from the else branch.
Author: Reynold Xin <rxin@databricks.com>
Closes#10827 from rxin/SPARK-12770.
Call `dealias` on local types to fix schema generation for abstract type members, such as
```scala
type KeyValue = (Int, String)
```
Add simple test
Author: Jakob Odersky <jodersky@gmail.com>
Closes#10749 from jodersky/aliased-schema.
JIRA: https://issues.apache.org/jira/browse/SPARK-12867
When intersecting one nullable column with one non-nullable column, the result will not contain any null. Thus, we can make nullability of `intersect` stricter.
liancheng Could you please check if the code changes are appropriate? Also added test cases to verify the results. Thanks!
Author: gatorsmile <gatorsmile@gmail.com>
Closes#10812 from gatorsmile/nullabilityIntersect.
Based on discussions in #10801, I'm submitting a pull request to rename ParserDialect to ParserInterface.
Author: Reynold Xin <rxin@databricks.com>
Closes#10817 from rxin/SPARK-12889.
In SPARK-10743 we wrap cast with `UnresolvedAlias` to give `Cast` a better alias if possible. However, for cases like `filter`, the `UnresolvedAlias` can't be resolved and actually we don't need a better alias for this case. This PR move the cast wrapping logic to `Column.named` so that we will only do it when we need a alias name.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10781 from cloud-fan/bug.
This pull request removes the public developer parser API for external parsers. Given everything a parser depends on (e.g. logical plans and expressions) are internal and not stable, external parsers will break with every release of Spark. It is a bad idea to create the illusion that Spark actually supports pluggable parsers. In addition, this also reduces incentives for 3rd party projects to contribute parse improvements back to Spark.
Author: Reynold Xin <rxin@databricks.com>
Closes#10801 from rxin/SPARK-12855.
I was reading this part of the analyzer code again and got confused by the difference between findWiderTypeForTwo and findTightestCommonTypeOfTwo.
I also simplified WidenSetOperationTypes to make it a lot simpler. The easiest way to review this one is to just read the original code, and the new code. The logic is super simple.
Author: Reynold Xin <rxin@databricks.com>
Closes#10802 from rxin/SPARK-12873.
This is the initial work for whole stage codegen, it support Projection/Filter/Range, we will continue work on this to support more physical operators.
A micro benchmark show that a query with range, filter and projection could be 3X faster then before.
It's turned on by default. For a tree that have at least two chained plans, a WholeStageCodegen will be inserted into it, for example, the following plan
```
Limit 10
+- Project [(id#5L + 1) AS (id + 1)#6L]
+- Filter ((id#5L & 1) = 1)
+- Range 0, 1, 4, 10, [id#5L]
```
will be translated into
```
Limit 10
+- WholeStageCodegen
+- Project [(id#1L + 1) AS (id + 1)#2L]
+- Filter ((id#1L & 1) = 1)
+- Range 0, 1, 4, 10, [id#1L]
```
Here is the call graph to generate Java source for A and B (A support codegen, but B does not):
```
* WholeStageCodegen Plan A FakeInput Plan B
* =========================================================================
*
* -> execute()
* |
* doExecute() --------> produce()
* |
* doProduce() -------> produce()
* |
* doProduce() ---> execute()
* |
* consume()
* doConsume() ------------|
* |
* doConsume() <----- consume()
```
A SparkPlan that support codegen need to implement doProduce() and doConsume():
```
def doProduce(ctx: CodegenContext): (RDD[InternalRow], String)
def doConsume(ctx: CodegenContext, child: SparkPlan, input: Seq[ExprCode]): String
```
Author: Davies Liu <davies@databricks.com>
Closes#10735 from davies/whole2.
We iterate the bytes to calculate hashCode before, but now we have `Murmur3_x86_32.hashUnsafeBytes` that don't require the bytes to be word algned, we should use that instead.
A simple benchmark shows it's about 3 X faster, benchmark code: https://gist.github.com/cloud-fan/fa77713ccebf0823b2ab#file-arrayhashbenchmark-scala
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10784 from cloud-fan/array-hashcode.
In this PR the new CatalystQl parser stack reaches grammar parity with the old Parser-Combinator based SQL Parser. This PR also replaces all uses of the old Parser, and removes it from the code base.
Although the existing Hive and SQL parser dialects were mostly the same, some kinks had to be worked out:
- The SQL Parser allowed syntax like ```APPROXIMATE(0.01) COUNT(DISTINCT a)```. In order to make this work we needed to hardcode approximate operators in the parser, or we would have to create an approximate expression. ```APPROXIMATE_COUNT_DISTINCT(a, 0.01)``` would also do the job and is much easier to maintain. So, this PR **removes** this keyword.
- The old SQL Parser supports ```LIMIT``` clauses in nested queries. This is **not supported** anymore. See https://github.com/apache/spark/pull/10689 for the rationale for this.
- Hive has a charset name char set literal combination it supports, for instance the following expression ```_ISO-8859-1 0x4341464562616265``` would yield this string: ```CAFEbabe```. Hive will only allow charset names to start with an underscore. This is quite annoying in spark because as soon as you use a tuple names will start with an underscore. In this PR we **remove** this feature from the parser. It would be quite easy to implement such a feature as an Expression later on.
- Hive and the SQL Parser treat decimal literals differently. Hive will turn any decimal into a ```Double``` whereas the SQL Parser would convert a non-scientific decimal into a ```BigDecimal```, and would turn a scientific decimal into a Double. We follow Hive's behavior here. The new parser supports a big decimal literal, for instance: ```81923801.42BD```, which can be used when a big decimal is needed.
cc rxin viirya marmbrus yhuai cloud-fan
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#10745 from hvanhovell/SPARK-12575-2.
We made it a `NamedExpression` to workaroud some hacky cases long time ago, and now seems it's safe to remove it.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10765 from cloud-fan/minor.
The goal of this PR is to eliminate unnecessary translations when there are back-to-back `MapPartitions` operations. In order to achieve this I also made the following simplifications:
- Operators no longer have hold encoders, instead they have only the expressions that they need. The benefits here are twofold: the expressions are visible to transformations so go through the normal resolution/binding process. now that they are visible we can change them on a case by case basis.
- Operators no longer have type parameters. Since the engine is responsible for its own type checking, having the types visible to the complier was an unnecessary complication. We still leverage the scala compiler in the companion factory when constructing a new operator, but after this the types are discarded.
Deferred to a follow up PR:
- Remove as much of the resolution/binding from Dataset/GroupedDataset as possible. We should still eagerly check resolution and throw an error though in the case of mismatches for an `as` operation.
- Eliminate serializations in more cases by adding more cases to `EliminateSerialization`
Author: Michael Armbrust <michael@databricks.com>
Closes#10747 from marmbrus/encoderExpressions.
The generated code for CaseWhen uses a control variable "got" to make sure we do not evaluate more branches once a branch is true. Changing that to generate just simple "if / else" would be slightly more efficient.
This closes#10737.
Author: Reynold Xin <rxin@databricks.com>
Closes#10755 from rxin/SPARK-12771.
This PR makes bucketing and exchange share one common hash algorithm, so that we can guarantee the data distribution is same between shuffle and bucketed data source, which enables us to only shuffle one side when join a bucketed table and a normal one.
This PR also fixes the tests that are broken by the new hash behaviour in shuffle.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10703 from cloud-fan/use-hash-expr-in-shuffle.
This pull request rewrites CaseWhen expression to break the single, monolithic "branches" field into a sequence of tuples (Seq[(condition, value)]) and an explicit optional elseValue field.
Prior to this pull request, each even position in "branches" represents the condition for each branch, and each odd position represents the value for each branch. The use of them have been pretty confusing with a lot sliding windows or grouped(2) calls.
Author: Reynold Xin <rxin@databricks.com>
Closes#10734 from rxin/simplify-case.
Fix the style violation (space before , and :).
This PR is a followup for #10643 and rework of #10685 .
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#10732 from sarutak/SPARK-12692-followup-sql.
This patch removes CaseKeyWhen expression and replaces it with a factory method that generates the equivalent CaseWhen. This reduces the amount of code we'd need to maintain in the future for both code generation and optimizer.
Note that we introduced CaseKeyWhen to avoid duplicate evaluations of the key. This is no longer a problem because we now have common subexpression elimination.
Author: Reynold Xin <rxin@databricks.com>
Closes#10722 from rxin/SPARK-12768.
This pull request does a few small things:
1. Separated if simplification from BooleanSimplification and created a new rule SimplifyConditionals. In the future we can also simplify other conditional expressions here.
2. Added unit test for SimplifyConditionals.
3. Renamed SimplifyCaseConversionExpressionsSuite to SimplifyStringCaseConversionSuite
Author: Reynold Xin <rxin@databricks.com>
Closes#10716 from rxin/SPARK-12762.
Fix the style violation (space before , and :).
This PR is a followup for #10643.
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#10718 from sarutak/SPARK-12692-followup-sql.
Scala syntax allows binary case classes to be used as infix operator in pattern matching. This PR makes use of this syntax sugar to make `BooleanSimplification` more readable.
Author: Cheng Lian <lian@databricks.com>
Closes#10445 from liancheng/boolean-simplification-simplification.
The PR allows us to use the new SQL parser to parse SQL expressions such as: ```1 + sin(x*x)```
We enable this functionality in this PR, but we will not start using this actively yet. This will be done as soon as we have reached grammar parity with the existing parser stack.
cc rxin
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#10649 from hvanhovell/SPARK-12576.
Turn import ordering violations into build errors, plus a few adjustments
to account for how the checker behaves. I'm a little on the fence about
whether the existing code is right, but it's easier to appease the checker
than to discuss what's the more correct order here.
Plus a few fixes to imports that cropped in since my recent cleanups.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#10612 from vanzin/SPARK-3873-enable.
This PR tries to enable Spark SQL to convert resolved logical plans back to SQL query strings. For now, the major use case is to canonicalize Spark SQL native view support. The major entry point is `SQLBuilder.toSQL`, which returns an `Option[String]` if the logical plan is recognized.
The current version is still in WIP status, and is quite limited. Known limitations include:
1. The logical plan must be analyzed but not optimized
The optimizer erases `Subquery` operators, which contain necessary scope information for SQL generation. Future versions should be able to recover erased scope information by inserting subqueries when necessary.
1. The logical plan must be created using HiveQL query string
Query plans generated by composing arbitrary DataFrame API combinations are not supported yet. Operators within these query plans need to be rearranged into a canonical form that is more suitable for direct SQL generation. For example, the following query plan
```
Filter (a#1 < 10)
+- MetastoreRelation default, src, None
```
need to be canonicalized into the following form before SQL generation:
```
Project [a#1, b#2, c#3]
+- Filter (a#1 < 10)
+- MetastoreRelation default, src, None
```
Otherwise, the SQL generation process will have to handle a large number of special cases.
1. Only a fraction of expressions and basic logical plan operators are supported in this PR
Currently, 95.7% (1720 out of 1798) query plans in `HiveCompatibilitySuite` can be successfully converted to SQL query strings.
Known unsupported components are:
- Expressions
- Part of math expressions
- Part of string expressions (buggy?)
- Null expressions
- Calendar interval literal
- Part of date time expressions
- Complex type creators
- Special `NOT` expressions, e.g. `NOT LIKE` and `NOT IN`
- Logical plan operators/patterns
- Cube, rollup, and grouping set
- Script transformation
- Generator
- Distinct aggregation patterns that fit `DistinctAggregationRewriter` analysis rule
- Window functions
Support for window functions, generators, and cubes etc. will be added in follow-up PRs.
This PR leverages `HiveCompatibilitySuite` for testing SQL generation in a "round-trip" manner:
* For all select queries, we try to convert it back to SQL
* If the query plan is convertible, we parse the generated SQL into a new logical plan
* Run the new logical plan instead of the original one
If the query plan is inconvertible, the test case simply falls back to the original logic.
TODO
- [x] Fix failed test cases
- [x] Support for more basic expressions and logical plan operators (e.g. distinct aggregation etc.)
- [x] Comments and documentation
Author: Cheng Lian <lian@databricks.com>
Closes#10541 from liancheng/sql-generation.
JIRA: https://issues.apache.org/jira/browse/SPARK-12687
Some queries such as `(select 1 as a) union (select 2 as a)` can't work. This patch fixes it.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#10660 from viirya/fix-union.
Fix most build warnings: mostly deprecated API usages. I'll annotate some of the changes below. CC rxin who is leading the charge to remove the deprecated APIs.
Author: Sean Owen <sowen@cloudera.com>
Closes#10570 from srowen/SPARK-12618.
Use multi-line string literals for ExpressionDescription with ``// scalastyle:off line.size.limit`` and ``// scalastyle:on line.size.limit``
The policy is here, as describe at https://github.com/apache/spark/pull/10488
Let's use multi-line string literals. If we have to have a line with more than 100 characters, let's use ``// scalastyle:off line.size.limit`` and ``// scalastyle:on line.size.limit`` to just bypass the line number requirement.
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#10524 from kiszk/SPARK-12580.
To avoid to have a huge Java source (over 64K loc), that can't be compiled.
cc hvanhovell
Author: Davies Liu <davies@databricks.com>
Closes#10624 from davies/split_ident.
This PR moves a major part of the new SQL parser to Catalyst. This is a prelude to start using this parser for all of our SQL parsing. The following key changes have been made:
The ANTLR Parser & Supporting classes have been moved to the Catalyst project. They are now part of the ```org.apache.spark.sql.catalyst.parser``` package. These classes contained quite a bit of code that was originally from the Hive project, I have added aknowledgements whenever this applied. All Hive dependencies have been factored out. I have also taken this chance to clean-up the ```ASTNode``` class, and to improve the error handling.
The HiveQl object that provides the functionality to convert an AST into a LogicalPlan has been refactored into three different classes, one for every SQL sub-project:
- ```CatalystQl```: This implements Query and Expression parsing functionality.
- ```SparkQl```: This is a subclass of CatalystQL and provides SQL/Core only functionality such as Explain and Describe.
- ```HiveQl```: This is a subclass of ```SparkQl``` and this adds Hive-only functionality to the parser such as Analyze, Drop, Views, CTAS & Transforms. This class still depends on Hive.
cc rxin
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#10583 from hvanhovell/SPARK-12575.
JIRA: https://issues.apache.org/jira/browse/SPARK-12439
In toCatalystArray, we should look at the data type returned by dataTypeFor instead of silentSchemaFor, to determine if the element is native type. An obvious problem is when the element is Option[Int] class, catalsilentSchemaFor will return Int, then we will wrongly recognize the element is native type.
There is another problem when using Option as array element. When we encode data like Seq(Some(1), Some(2), None) with encoder, we will use MapObjects to construct an array for it later. But in MapObjects, we don't check if the return value of lambdaFunction is null or not. That causes a bug that the decoded data for Seq(Some(1), Some(2), None) would be Seq(1, 2, -1), instead of Seq(1, 2, null).
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#10391 from viirya/fix-catalystarray.
address comments in #10435
This makes the API easier to use if user programmatically generate the call to hash, and they will get analysis exception if the arguments of hash is empty.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10588 from cloud-fan/hash.
just write the arguments into unsafe row and use murmur3 to calculate hash code
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10435 from cloud-fan/hash-expr.
The reader was previously not setting the row length meaning it was wrong if there were variable
length columns. This problem does not manifest usually, since the value in the column is correct and
projecting the row fixes the issue.
Author: Nong Li <nong@databricks.com>
Closes#10576 from nongli/spark-12589.
This PR enable cube/rollup as function, so they can be used as this:
```
select a, b, sum(c) from t group by rollup(a, b)
```
Author: Davies Liu <davies@databricks.com>
Closes#10522 from davies/rollup.
It is currently possible to change the values of the supposedly immutable ```GenericRow``` and ```GenericInternalRow``` classes. This is caused by the fact that scala's ArrayOps ```toArray``` (returned by calling ```toSeq```) will return the backing array instead of a copy. This PR fixes this problem.
This PR was inspired by https://github.com/apache/spark/pull/10374 by apo1.
cc apo1 sarutak marmbrus cloud-fan nongli (everyone in the previous conversation).
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#10553 from hvanhovell/SPARK-12421.
Avoiding the the No such table exception and throwing analysis exception as per the bug: SPARK-12533
Author: thomastechs <thomas.sebastian@tcs.com>
Closes#10529 from thomastechs/topic-branch.
Right now, numFields will be passed in by pointTo(), then bitSetWidthInBytes is calculated, making pointTo() a little bit heavy.
It should be part of constructor of UnsafeRow.
Author: Davies Liu <davies@databricks.com>
Closes#10528 from davies/numFields.
Most of cases we should propagate null when call `NewInstance`, and so far there is only one case we should stop null propagation: create product/java bean. So I think it makes more sense to propagate null by dafault.
This also fixes a bug when encode null array/map, which is firstly discovered in https://github.com/apache/spark/pull/10401
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10443 from cloud-fan/encoder.
```
org.apache.spark.sql.AnalysisException: cannot resolve 'value' given input columns text;
```
lets put a `:` after `columns` and put the columns in `[]` so that they match the toString of DataFrame.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#10518 from gatorsmile/improveAnalysisExceptionMsg.
In Spark we allow UDFs to declare its expected input types in order to apply type coercion. The expected input type parameter takes a Seq[DataType] and uses Nil when no type coercion is applied. It makes more sense to take Option[Seq[DataType]] instead, so we can differentiate a no-arg function vs function with no expected input type specified.
Author: Reynold Xin <rxin@databricks.com>
Closes#10504 from rxin/SPARK-12549.
When explain any plan with Generate, we will see an exclamation mark in the plan. Normally, when we see this mark, it means the plan has an error. This PR is to correct the `missingInput` in `Generate`.
For example,
```scala
val df = Seq((1, "a b c"), (2, "a b"), (3, "a")).toDF("number", "letters")
val df2 =
df.explode('letters) {
case Row(letters: String) => letters.split(" ").map(Tuple1(_)).toSeq
}
df2.explain(true)
```
Before the fix, the plan is like
```
== Parsed Logical Plan ==
'Generate UserDefinedGenerator('letters), true, false, None
+- Project [_1#0 AS number#2,_2#1 AS letters#3]
+- LocalRelation [_1#0,_2#1], [[1,a b c],[2,a b],[3,a]]
== Analyzed Logical Plan ==
number: int, letters: string, _1: string
Generate UserDefinedGenerator(letters#3), true, false, None, [_1#8]
+- Project [_1#0 AS number#2,_2#1 AS letters#3]
+- LocalRelation [_1#0,_2#1], [[1,a b c],[2,a b],[3,a]]
== Optimized Logical Plan ==
Generate UserDefinedGenerator(letters#3), true, false, None, [_1#8]
+- LocalRelation [number#2,letters#3], [[1,a b c],[2,a b],[3,a]]
== Physical Plan ==
!Generate UserDefinedGenerator(letters#3), true, false, [number#2,letters#3,_1#8]
+- LocalTableScan [number#2,letters#3], [[1,a b c],[2,a b],[3,a]]
```
**Updates**: The same issues are also found in the other four Dataset operators: `MapPartitions`/`AppendColumns`/`MapGroups`/`CoGroup`. Fixed all these four.
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#10393 from gatorsmile/generateExplain.
Moved (case) classes Strategy, Once, FixedPoint and Batch to the companion object. This is necessary if we want to have the Optimizer easily extendable in the following sense: Usually a user wants to add additional rules, and just take the ones that are already there. However, inner classes made that impossible since the code did not compile
This allows easy extension of existing Optimizers see the DefaultOptimizerExtendableSuite for a corresponding test case.
Author: Stephan Kessler <stephan.kessler@sap.com>
Closes#10174 from stephankessler/SPARK-7727.
Accessing null elements in an array field fails when tungsten is enabled.
It works in Spark 1.3.1, and in Spark > 1.5 with Tungsten disabled.
This PR solves this by checking if the accessed element in the array field is null, in the generated code.
Example:
```
// Array of String
case class AS( as: Seq[String] )
val dfAS = sc.parallelize( Seq( AS ( Seq("a",null,"b") ) ) ).toDF
dfAS.registerTempTable("T_AS")
for (i <- 0 to 2) { println(i + " = " + sqlContext.sql(s"select as[$i] from T_AS").collect.mkString(","))}
```
With Tungsten disabled:
```
0 = [a]
1 = [null]
2 = [b]
```
With Tungsten enabled:
```
0 = [a]
15/12/22 09:32:50 ERROR Executor: Exception in task 7.0 in stage 1.0 (TID 15)
java.lang.NullPointerException
at org.apache.spark.sql.catalyst.expressions.UnsafeRowWriters$UTF8StringWriter.getSize(UnsafeRowWriters.java:90)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
at org.apache.spark.sql.execution.TungstenProject$$anonfun$3$$anonfun$apply$3.apply(basicOperators.scala:90)
at org.apache.spark.sql.execution.TungstenProject$$anonfun$3$$anonfun$apply$3.apply(basicOperators.scala:88)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
```
Author: pierre-borckmans <pierre.borckmans@realimpactanalytics.com>
Closes#10429 from pierre-borckmans/SPARK-12477_Tungsten-Projection-Null-Element-In-Array.
When the filter is ```"b in ('1', '2')"```, the filter is not pushed down to Parquet. Thanks!
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#10278 from gatorsmile/parquetFilterNot.
When creating extractors for product types (i.e. case classes and tuples), a null check is missing, thus we always assume input product values are non-null.
This PR adds a null check in the extractor expression for product types. The null check is stripped off for top level product fields, which are mapped to the outermost `Row`s, since they can't be null.
Thanks cloud-fan for helping investigating this issue!
Author: Cheng Lian <lian@databricks.com>
Closes#10431 from liancheng/spark-12478.top-level-null-field.
Compare both left and right side of the case expression ignoring nullablity when checking for type equality.
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#10156 from dilipbiswal/spark-12102.
First try, not sure how much information we need to provide in the usage part.
Author: Xiu Guo <xguo27@gmail.com>
Closes#10423 from xguo27/SPARK-12456.
This PR adds a new expression `AssertNotNull` to ensure non-nullable fields of products and case classes don't receive null values at runtime.
Author: Cheng Lian <lian@databricks.com>
Closes#10331 from liancheng/dataset-nullability-check.
Based on the suggestions from marmbrus , added logical/physical operators for Range for improving the performance.
Also added another API for resolving the JIRA Spark-12150.
Could you take a look at my implementation, marmbrus ? If not good, I can rework it. : )
Thank you very much!
Author: gatorsmile <gatorsmile@gmail.com>
Closes#10335 from gatorsmile/rangeOperators.
When a DataFrame or Dataset has a long schema, we should intelligently truncate to avoid flooding the screen with unreadable information.
// Standard output
[a: int, b: int]
// Truncate many top level fields
[a: int, b, string ... 10 more fields]
// Truncate long inner structs
[a: struct<a: Int ... 10 more fields>]
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#10373 from dilipbiswal/spark-12398.
Now `StaticInvoke` receives `Any` as a object and `StaticInvoke` can be serialized but sometimes the object passed is not serializable.
For example, following code raises Exception because `RowEncoder#extractorsFor` invoked indirectly makes `StaticInvoke`.
```
case class TimestampContainer(timestamp: java.sql.Timestamp)
val rdd = sc.parallelize(1 to 2).map(_ => TimestampContainer(System.currentTimeMillis))
val df = rdd.toDF
val ds = df.as[TimestampContainer]
val rdd2 = ds.rdd <----------------- invokes extractorsFor indirectory
```
I'll add test cases.
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Author: Michael Armbrust <michael@databricks.com>
Closes#10357 from sarutak/SPARK-12404.
This could simplify the generated code for expressions that is not nullable.
This PR fix lots of bugs about nullability.
Author: Davies Liu <davies@databricks.com>
Closes#10333 from davies/skip_nullable.
Description of the problem from cloud-fan
Actually this line: https://github.com/apache/spark/blob/branch-1.5/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala#L689
When we use `selectExpr`, we pass in `UnresolvedFunction` to `DataFrame.select` and fall in the last case. A workaround is to do special handling for UDTF like we did for `explode`(and `json_tuple` in 1.6), wrap it with `MultiAlias`.
Another workaround is using `expr`, for example, `df.select(expr("explode(a)").as(Nil))`, I think `selectExpr` is no longer needed after we have the `expr` function....
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#9981 from dilipbiswal/spark-11619.
This PR removes Hive windows functions from Spark and replaces them with (native) Spark ones. The PR is on par with Hive in terms of features.
This has the following advantages:
* Better memory management.
* The ability to use spark UDAFs in Window functions.
cc rxin / yhuai
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#9819 from hvanhovell/SPARK-8641-2.
I think it was a mistake, and we have not catched it so far until https://github.com/apache/spark/pull/10260 which begin to check if the `fromRowExpression` is resolved.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10263 from cloud-fan/encoder.
Currently, we could generate different plans for query with single distinct (depends on spark.sql.specializeSingleDistinctAggPlanning), one works better on low cardinality columns, the other
works better for high cardinality column (default one).
This PR change to generate a single plan (three aggregations and two exchanges), which work better in both cases, then we could safely remove the flag `spark.sql.specializeSingleDistinctAggPlanning` (introduced in 1.6).
For a query like `SELECT COUNT(DISTINCT a) FROM table` will be
```
AGG-4 (count distinct)
Shuffle to a single reducer
Partial-AGG-3 (count distinct, no grouping)
Partial-AGG-2 (grouping on a)
Shuffle by a
Partial-AGG-1 (grouping on a)
```
This PR also includes large refactor for aggregation (reduce 500+ lines of code)
cc yhuai nongli marmbrus
Author: Davies Liu <davies@databricks.com>
Closes#10228 from davies/single_distinct.
in https://github.com/apache/spark/pull/10133 we found that, we shoud ensure the children of `TreeNode` are all accessible in the `productIterator`, or the behavior will be very confusing.
In this PR, I try to fix this problem by expsing the `loopVar`.
This also fixes SPARK-12131 which is caused by the hacky `MapObjects`.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10239 from cloud-fan/map-objects.
Delays application of ResolvePivot until all aggregates are resolved to prevent problems with UnresolvedFunction and adds unit test
Author: Andrew Ray <ray.andrew@gmail.com>
Closes#10202 from aray/sql-pivot-unresolved-function.
This PR is to add three more data types into Encoder, including `BigDecimal`, `Date` and `Timestamp`.
marmbrus cloud-fan rxin Could you take a quick look at these three types? Not sure if it can be merged to 1.6. Thank you very much!
Author: gatorsmile <gatorsmile@gmail.com>
Closes#10188 from gatorsmile/dataTypesinEncoder.
checked with hive, greatest/least should cast their children to a tightest common type,
i.e. `(int, long) => long`, `(int, string) => error`, `(decimal(10,5), decimal(5, 10)) => error`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10196 from cloud-fan/type-coercion.
Currently, the order of joins is exactly the same as SQL query, some conditions may not pushed down to the correct join, then those join will become cross product and is extremely slow.
This patch try to re-order the inner joins (which are common in SQL query), pick the joins that have self-contain conditions first, delay those that does not have conditions.
After this patch, the TPCDS query Q64/65 can run hundreds times faster.
cc marmbrus nongli
Author: Davies Liu <davies@databricks.com>
Closes#10073 from davies/reorder_joins.
When \u appears in a comment block (i.e. in /**/), code gen will break. So, in Expression and CodegenFallback, we escape \u to \\u.
yhuai Please review it. I did reproduce it and it works after the fix. Thanks!
Author: gatorsmile <gatorsmile@gmail.com>
Closes#10155 from gatorsmile/escapeU.
We should upgrade to SBT 0.13.9, since this is a requirement in order to use SBT's new Maven-style resolution features (which will be done in a separate patch, because it's blocked by some binary compatibility issues in the POM reader plugin).
I also upgraded Scalastyle to version 0.8.0, which was necessary in order to fix a Scala 2.10.5 compatibility issue (see https://github.com/scalastyle/scalastyle/issues/156). The newer Scalastyle is slightly stricter about whitespace surrounding tokens, so I fixed the new style violations.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#10112 from JoshRosen/upgrade-to-sbt-0.13.9.
This replaces https://github.com/apache/spark/pull/9696
Invoke Checkstyle and print any errors to the console, failing the step.
Use Google's style rules modified according to
https://cwiki.apache.org/confluence/display/SPARK/Spark+Code+Style+Guide
Some important checks are disabled (see TODOs in `checkstyle.xml`) due to
multiple violations being present in the codebase.
Suggest fixing those TODOs in a separate PR(s).
More on Checkstyle can be found on the [official website](http://checkstyle.sourceforge.net/).
Sample output (from [build 46345](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/46345/consoleFull)) (duplicated because I run the build twice with different profiles):
> Checkstyle checks failed at following occurrences:
[ERROR] src/main/java/org/apache/spark/sql/execution/datasources/parquet/UnsafeRowParquetRecordReader.java:[217,7] (coding) MissingSwitchDefault: switch without "default" clause.
> [ERROR] src/main/java/org/apache/spark/sql/execution/datasources/parquet/SpecificParquetRecordReaderBase.java:[198,10] (modifier) ModifierOrder: 'protected' modifier out of order with the JLS suggestions.
> [ERROR] src/main/java/org/apache/spark/sql/execution/datasources/parquet/UnsafeRowParquetRecordReader.java:[217,7] (coding) MissingSwitchDefault: switch without "default" clause.
> [ERROR] src/main/java/org/apache/spark/sql/execution/datasources/parquet/SpecificParquetRecordReaderBase.java:[198,10] (modifier) ModifierOrder: 'protected' modifier out of order with the JLS suggestions.
> [error] running /home/jenkins/workspace/SparkPullRequestBuilder2/dev/lint-java ; received return code 1
Also fix some of the minor violations that didn't require sweeping changes.
Apologies for the previous botched PRs - I finally figured out the issue.
cr: JoshRosen, pwendell
> I state that the contribution is my original work, and I license the work to the project under the project's open source license.
Author: Dmitry Erastov <derastov@gmail.com>
Closes#9867 from dskrvk/master.
When examining plans of complex queries with multiple joins, a pain point of mine is that, it's hard to immediately see the sibling node of a specific query plan node. This PR adds tree lines for the tree string of a `TreeNode`, so that the result can be visually more intuitive.
Author: Cheng Lian <lian@databricks.com>
Closes#10099 from liancheng/prettier-tree-string.
Following up #10038.
We can use bitmasks to determine which grouping expressions need to be set as nullable.
cc yhuai
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#10067 from viirya/fix-cube-following.
create java version of `constructorFor` and `extractorFor` in `JavaTypeInference`
Author: Wenchen Fan <wenchen@databricks.com>
This patch had conflicts when merged, resolved by
Committer: Michael Armbrust <michael@databricks.com>
Closes#9937 from cloud-fan/pojo.
When we build the `fromRowExpression` for an encoder, we set up a lot of "unresolved" stuff and lost the required data type, which may lead to runtime error if the real type doesn't match the encoder's schema.
For example, we build an encoder for `case class Data(a: Int, b: String)` and the real type is `[a: int, b: long]`, then we will hit runtime error and say that we can't construct class `Data` with int and long, because we lost the information that `b` should be a string.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9840 from cloud-fan/err-msg.
JIRA: https://issues.apache.org/jira/browse/SPARK-11949
The result of cube plan uses incorrect schema. The schema of cube result should set nullable property to true because the grouping expressions will have null values.
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#10038 from viirya/fix-cube.
JIRA: https://issues.apache.org/jira/browse/SPARK-12018
The code of common subexpression elimination can be factored and simplified. Some unnecessary variables can be removed.
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#10009 from viirya/refactor-subexpr-eliminate.
In https://github.com/apache/spark/pull/9409 we enabled multi-column counting. The approach taken in that PR introduces a bit of overhead by first creating a row only to check if all of the columns are non-null.
This PR fixes that technical debt. Count now takes multiple columns as its input. In order to make this work I have also added support for multiple columns in the single distinct code path.
cc yhuai
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#10015 from hvanhovell/SPARK-12024.
When calling `get_json_object` for the following two cases, both results are `"null"`:
```scala
val tuple: Seq[(String, String)] = ("5", """{"f1": null}""") :: Nil
val df: DataFrame = tuple.toDF("key", "jstring")
val res = df.select(functions.get_json_object($"jstring", "$.f1")).collect()
```
```scala
val tuple2: Seq[(String, String)] = ("5", """{"f1": "null"}""") :: Nil
val df2: DataFrame = tuple2.toDF("key", "jstring")
val res3 = df2.select(functions.get_json_object($"jstring", "$.f1")).collect()
```
Fixed the problem and also added a test case.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#10018 from gatorsmile/get_json_object.
This is a followup for https://github.com/apache/spark/pull/9959.
I added more documentation and rewrote some monadic code into simpler ifs.
Author: Reynold Xin <rxin@databricks.com>
Closes#9995 from rxin/SPARK-11973.
this is based on https://github.com/apache/spark/pull/9844, with some bug fix and clean up.
The problems is that, normal operator should be resolved based on its child, but `Sort` operator can also be resolved based on its grandchild. So we have 3 rules that can resolve `Sort`: `ResolveReferences`, `ResolveSortReferences`(if grandchild is `Project`) and `ResolveAggregateFunctions`(if grandchild is `Aggregate`).
For example, `select c1 as a , c2 as b from tab group by c1, c2 order by a, c2`, we need to resolve `a` and `c2` for `Sort`. Firstly `a` will be resolved in `ResolveReferences` based on its child, and when we reach `ResolveAggregateFunctions`, we will try to resolve both `a` and `c2` based on its grandchild, but failed because `a` is not a legal aggregate expression.
whoever merge this PR, please give the credit to dilipbiswal
Author: Dilip Biswal <dbiswal@us.ibm.com>
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9961 from cloud-fan/sort.
Currently, filter can't be pushed through aggregation with alias or literals, this patch fix that.
After this patch, the time of TPC-DS query 4 go down to 13 seconds from 141 seconds (10x improvements).
cc nongli yhuai
Author: Davies Liu <davies@databricks.com>
Closes#9959 from davies/push_filter2.
Right now, the expended start will include the name of expression as prefix for column, that's not better than without expending, we should not have the prefix.
Author: Davies Liu <davies@databricks.com>
Closes#9984 from davies/expand_star.
Currently pivot's signature looks like
```scala
scala.annotation.varargs
def pivot(pivotColumn: Column, values: Column*): GroupedData
scala.annotation.varargs
def pivot(pivotColumn: String, values: Any*): GroupedData
```
I think we can remove the one that takes "Column" types, since callers should always be passing in literals. It'd also be more clear if the values are not varargs, but rather Seq or java.util.List.
I also made similar changes for Python.
Author: Reynold Xin <rxin@databricks.com>
Closes#9929 from rxin/SPARK-11946.
we should pass in resolved encodera to logical `CoGroup` and bind them in physical `CoGroup`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9928 from cloud-fan/cogroup.
We should use `InternalRow.isNullAt` to check if the field is null before calling `InternalRow.getXXX`
Thanks gatorsmile who discovered this bug.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9904 from cloud-fan/null.
Can someone review my code to make sure I'm not missing anything? Thanks!
Author: Xiu Guo <xguo27@gmail.com>
Author: Xiu Guo <guoxi@us.ibm.com>
Closes#9612 from xguo27/SPARK-11628.
1. Renamed map to mapGroup, flatMap to flatMapGroup.
2. Renamed asKey -> keyAs.
3. Added more documentation.
4. Changed type parameter T to V on GroupedDataset.
5. Added since versions for all functions.
Author: Reynold Xin <rxin@databricks.com>
Closes#9880 from rxin/SPARK-11899.
seems scala 2.11 doesn't support: define private methods in `trait xxx` and use it in `object xxx extend xxx`.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9879 from cloud-fan/follow.
This mainly moves SqlNewHadoopRDD to the sql package. There is some state that is
shared between core and I've left that in core. This allows some other associated
minor cleanup.
Author: Nong Li <nong@databricks.com>
Closes#9845 from nongli/spark-11787.
#theScaryParts (i.e. changes to the repl, executor classloaders and codegen)...
Author: Michael Armbrust <michael@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#9825 from marmbrus/dataset-replClasses2.
Hive has since changed this behavior as well. https://issues.apache.org/jira/browse/HIVE-3454
Author: Nong Li <nong@databricks.com>
Author: Nong Li <nongli@gmail.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#9685 from nongli/spark-11724.
before this PR, when users try to get an encoder for an un-supported class, they will only get a very simple error message like `Encoder for type xxx is not supported`.
After this PR, the error message become more friendly, for example:
```
No Encoder found for abc.xyz.NonEncodable
- array element class: "abc.xyz.NonEncodable"
- field (class: "scala.Array", name: "arrayField")
- root class: "abc.xyz.AnotherClass"
```
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9810 from cloud-fan/error-message.
JIRA: https://issues.apache.org/jira/browse/SPARK-11817
Instead of return None, we should truncate the fractional seconds to prevent inserting NULL.
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#9834 from viirya/truncate-fractional-sec.
This PR has the following optimization:
1) The greatest/least already does the null-check, so the `If` and `IsNull` are not necessary.
2) In greatest/least, it should initialize the result using the first child (removing one block).
3) For primitive types, the generated greater expression is too complicated (`a > b ? 1 : (a < b) ? -1 : 0) > 0`), should be as simple as `a > b`
Combine these optimization, this could improve the performance of `ss_max` query by 30%.
Author: Davies Liu <davies@databricks.com>
Closes#9846 from davies/improve_max.
Fixes bug with grouping sets (including cube/rollup) where aggregates that included grouping expressions would return the wrong (null) result.
Also simplifies the analyzer rule a bit and leaves column pruning to the optimizer.
Added multiple unit tests to DataFrameAggregateSuite and verified it passes hive compatibility suite:
```
build/sbt -Phive -Dspark.hive.whitelist='groupby.*_grouping.*' 'test-only org.apache.spark.sql.hive.execution.HiveCompatibilitySuite'
```
This is an alternative to pr https://github.com/apache/spark/pull/9419 but I think its better as it simplifies the analyzer rule instead of adding another special case to it.
Author: Andrew Ray <ray.andrew@gmail.com>
Closes#9815 from aray/groupingset-agg-fix.
After some experiment, I found it's not convenient to have separate encoder builders: `FlatEncoder` and `ProductEncoder`. For example, when create encoders for `ScalaUDF`, we have no idea if the type `T` is flat or not. So I revert the splitting change in https://github.com/apache/spark/pull/9693, while still keeping the bug fixes and tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9726 from cloud-fan/follow.
The impact of this change is for a query that has a single distinct column and does not have any grouping expression like
`SELECT COUNT(DISTINCT a) FROM table`
The plan will be changed from
```
AGG-2 (count distinct)
Shuffle to a single reducer
Partial-AGG-2 (count distinct)
AGG-1 (grouping on a)
Shuffle by a
Partial-AGG-1 (grouping on 1)
```
to the following one (1.5 uses this)
```
AGG-2
AGG-1 (grouping on a)
Shuffle to a single reducer
Partial-AGG-1(grouping on a)
```
The first plan is more robust. However, to better benchmark the impact of this change, we should use 1.5's plan and use the conf of `spark.sql.specializeSingleDistinctAggPlanning` to control the plan.
Author: Yin Huai <yhuai@databricks.com>
Closes#9828 from yhuai/distinctRewriter.
We currently rely on the optimizer's constant folding to replace current_timestamp and current_date. However, this can still result in different values for different instances of current_timestamp/current_date if the optimizer is not running fast enough.
A better solution is to replace these functions in the analyzer in one shot.
Author: Reynold Xin <rxin@databricks.com>
Closes#9833 from rxin/SPARK-11849.
This patch adds an alternate to the Parquet RecordReader from the parquet-mr project
that is much faster for flat schemas. Instead of using the general converter mechanism
from parquet-mr, this directly uses the lower level APIs from parquet-columnar and a
customer RecordReader that directly assembles into UnsafeRows.
This is optionally disabled and only used for supported schemas.
Using the tpcds store sales table and doing a sum of increasingly more columns, the results
are:
For 1 Column:
Before: 11.3M rows/second
After: 18.2M rows/second
For 2 Columns:
Before: 7.2M rows/second
After: 11.2M rows/second
For 5 Columns:
Before: 2.9M rows/second
After: 4.5M rows/second
Author: Nong Li <nong@databricks.com>
Closes#9774 from nongli/parquet.
Also added some nicer error messages for incompatible types (private types and primitive types) for Kryo/Java encoder.
Author: Reynold Xin <rxin@databricks.com>
Closes#9823 from rxin/SPARK-11833.
Before this PR there were two things that would blow up if you called `df.as[MyClass]` if `MyClass` was defined in the REPL:
- [x] Because `classForName` doesn't work on the munged names returned by `tpe.erasure.typeSymbol.asClass.fullName`
- [x] Because we don't have anything to pass into the constructor for the `$outer` pointer.
Note that this PR is just adding the infrastructure for working with inner classes in encoder and is not yet sufficient to make them work in the REPL. Currently, the implementation show in 95cec7d413 is causing a bug that breaks code gen due to some interaction between janino and the `ExecutorClassLoader`. This will be addressed in a follow-up PR.
Author: Michael Armbrust <michael@databricks.com>
Closes#9602 from marmbrus/dataset-replClasses.
This patch refactors the existing Kryo encoder expressions and adds support for Java serialization.
Author: Reynold Xin <rxin@databricks.com>
Closes#9802 from rxin/SPARK-11810.
return Double.NaN for mean/average when count == 0 for all numeric types that is converted to Double, Decimal type continue to return null.
Author: JihongMa <linlin200605@gmail.com>
Closes#9705 from JihongMA/SPARK-11720.
If user use primitive parameters in UDF, there is no way for him to do the null-check for primitive inputs, so we are assuming the primitive input is null-propagatable for this case and return null if the input is null.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9770 from cloud-fan/udf.
I also found a bug with self-joins returning incorrect results in the Dataset API. Two test cases attached and filed SPARK-11803.
Author: Reynold Xin <rxin@databricks.com>
Closes#9789 from rxin/SPARK-11802.
Based on the comment of cloud-fan in https://github.com/apache/spark/pull/9216, update the AttributeReference's hashCode function by including the hashCode of the other attributes including name, nullable and qualifiers.
Here, I am not 100% sure if we should include name in the hashCode calculation, since the original hashCode calculation does not include it.
marmbrus cloud-fan Please review if the changes are good.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#9761 from gatorsmile/hashCodeNamedExpression.
In the previous method, fields.toArray will cast java.util.List[StructField] into Array[Object] which can not cast into Array[StructField], thus when invoking this method will throw "java.lang.ClassCastException: [Ljava.lang.Object; cannot be cast to [Lorg.apache.spark.sql.types.StructField;"
I directly cast java.util.List[StructField] into Array[StructField] in this patch.
Author: mayuanwen <mayuanwen@qiyi.com>
Closes#9649 from jackieMaKing/Spark-11679.
The randomly generated ArrayData used for the UDT `ExamplePoint` in `RowEncoderSuite` sometimes doesn't have enough elements. In this case, this test will fail. This patch is to fix it.
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#9757 from viirya/fix-randomgenerated-udt.
During executing PromoteStrings rule, if one side of binaryComparison is StringType and the other side is not StringType, the current code will promote(cast) the StringType to DoubleType, and if the StringType doesn't contain the numbers, it will get null value. So if it is doing <=> (NULL-safe equal) with Null, it will not filter anything, caused the problem reported by this jira.
I proposal to the changes through this PR, can you review my code changes ?
This problem only happen for <=>, other operators works fine.
scala> val filteredDF = df.filter(df("column") > (new Column(Literal(null))))
filteredDF: org.apache.spark.sql.DataFrame = [column: string]
scala> filteredDF.show
+------+
|column|
+------+
+------+
scala> val filteredDF = df.filter(df("column") === (new Column(Literal(null))))
filteredDF: org.apache.spark.sql.DataFrame = [column: string]
scala> filteredDF.show
+------+
|column|
+------+
+------+
scala> df.registerTempTable("DF")
scala> sqlContext.sql("select * from DF where 'column' = NULL")
res27: org.apache.spark.sql.DataFrame = [column: string]
scala> res27.show
+------+
|column|
+------+
+------+
Author: Kevin Yu <qyu@us.ibm.com>
Closes#9720 from kevinyu98/working_on_spark-11447.
This patch adds an alias for current_timestamp (now function).
Also fixes SPARK-9196 to re-enable the test case for current_timestamp.
Author: Reynold Xin <rxin@databricks.com>
Closes#9753 from rxin/SPARK-11768.
This fix is to change the equals method to check all of the specified fields for equality of AttributeReference.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#9216 from gatorsmile/namedExpressEqual.
Invocation of getters for type extending AnyVal returns default value (if field value is null) instead of throwing NPE. Please check comments for SPARK-11553 issue for more details.
Author: Bartlomiej Alberski <bartlomiej.alberski@allegrogroup.com>
Closes#9642 from alberskib/bugfix/SPARK-11553.
These 2 are very similar, we can consolidate them into one.
Also add tests for it and fix a bug.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9729 from cloud-fan/tuple.
JIRA: https://issues.apache.org/jira/browse/SPARK-11743
RowEncoder doesn't support UserDefinedType now. We should add the support for it.
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#9712 from viirya/rowencoder-udt.
code snippet to reproduce it:
```
TimeZone.setDefault(TimeZone.getTimeZone("Asia/Shanghai"))
val t = Timestamp.valueOf("1900-06-11 12:14:50.789")
val us = fromJavaTimestamp(t)
assert(getSeconds(us) === t.getSeconds)
```
it will be good to add a regression test for it, but the reproducing code need to change the default timezone, and even we change it back, the `lazy val defaultTimeZone` in `DataTimeUtils` is fixed.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9728 from cloud-fan/seconds.
also add more tests for encoders, and fix bugs that I found:
* when convert array to catalyst array, we can only skip element conversion for native types(e.g. int, long, boolean), not `AtomicType`(String is AtomicType but we need to convert it)
* we should also handle scala `BigDecimal` when convert from catalyst `Decimal`.
* complex map type should be supported
other issues that still in investigation:
* encode java `BigDecimal` and decode it back, seems we will loss precision info.
* when encode case class that defined inside a object, `ClassNotFound` exception will be thrown.
I'll remove unused code in a follow-up PR.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9693 from cloud-fan/split.
* rename `AppendColumn` to `AppendColumns` to be consistent with the physical plan name.
* clean up stale comments.
* always pass in resolved encoder to `TypedColumn.withInputType`(test added)
* enable a mistakenly disabled java test.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9688 from cloud-fan/follow.
This PR adds a new method, `reduce`, to `GroupedDataset`, which allows similar operations to `reduceByKey` on a traditional `PairRDD`.
```scala
val ds = Seq("abc", "xyz", "hello").toDS()
ds.groupBy(_.length).reduce(_ + _).collect() // not actually commutative :P
res0: Array(3 -> "abcxyz", 5 -> "hello")
```
While implementing this method and its test cases several more deficiencies were found in our encoder handling. Specifically, in order to support positional resolution, named resolution and tuple composition, it is important to keep the unresolved encoder around and to use it when constructing new `Datasets` with the same object type but different output attributes. We now divide the encoder lifecycle into three phases (that mirror the lifecycle of standard expressions) and have checks at various boundaries:
- Unresoved Encoders: all users facing encoders (those constructed by implicits, static methods, or tuple composition) are unresolved, meaning they have only `UnresolvedAttributes` for named fields and `BoundReferences` for fields accessed by ordinal.
- Resolved Encoders: internal to a `[Grouped]Dataset` the encoder is resolved, meaning all input has been resolved to a specific `AttributeReference`. Any encoders that are placed into a logical plan for use in object construction should be resolved.
- BoundEncoder: Are constructed by physical plans, right before actual conversion from row -> object is performed.
It is left to future work to add explicit checks for resolution and provide good error messages when it fails. We might also consider enforcing the above constraints in the type system (i.e. `fromRow` only exists on a `ResolvedEncoder`), but we should probably wait before spending too much time on this.
Author: Michael Armbrust <michael@databricks.com>
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9673 from marmbrus/pr/9628.
switched stddev support from DeclarativeAggregate to ImperativeAggregate.
Author: JihongMa <linlin200605@gmail.com>
Closes#9380 from JihongMA/SPARK-11420.
`to_unix_timestamp` is the deterministic version of `unix_timestamp`, as it accepts at least one parameters.
Since the behavior here is quite similar to `unix_timestamp`, I think the dataframe API is not necessary here.
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#9347 from adrian-wang/to_unix_timestamp.
This adds a pivot method to the dataframe api.
Following the lead of cube and rollup this adds a Pivot operator that is translated into an Aggregate by the analyzer.
Currently the syntax is like:
~~courseSales.pivot(Seq($"year"), $"course", Seq("dotNET", "Java"), sum($"earnings"))~~
~~Would we be interested in the following syntax also/alternatively? and~~
courseSales.groupBy($"year").pivot($"course", "dotNET", "Java").agg(sum($"earnings"))
//or
courseSales.groupBy($"year").pivot($"course").agg(sum($"earnings"))
Later we can add it to `SQLParser`, but as Hive doesn't support it we cant add it there, right?
~~Also what would be the suggested Java friendly method signature for this?~~
Author: Andrew Ray <ray.andrew@gmail.com>
Closes#7841 from aray/sql-pivot.
We need to support custom classes like java beans and combine them into tuple, and it's very hard to do it with the TypeTag-based approach.
We should keep only the compose-based way to create tuple encoder.
This PR also move `Encoder` to `org.apache.spark.sql`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9567 from cloud-fan/java.
This PR is a 2nd follow-up for [SPARK-9241](https://issues.apache.org/jira/browse/SPARK-9241). It contains the following improvements:
* Fix for a potential bug in distinct child expression and attribute alignment.
* Improved handling of duplicate distinct child expressions.
* Added test for distinct UDAF with multiple children.
cc yhuai
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#9566 from hvanhovell/SPARK-9241-followup-2.
This patch adds the building blocks for codegening subexpr elimination and implements
it end to end for UnsafeProjection. The building blocks can be used to do the same thing
for other operators.
It introduces some utilities to compute common sub expressions. Expressions can be added to
this data structure. The expr and its children will be recursively matched against existing
expressions (ones previously added) and grouped into common groups. This is built using
the existing `semanticEquals`. It does not understand things like commutative or associative
expressions. This can be done as future work.
After building this data structure, the codegen process takes advantage of it by:
1. Generating a helper function in the generated class that computes the common
subexpression. This is done for all common subexpressions that have at least
two occurrences and the expression tree is sufficiently complex.
2. When generating the apply() function, if the helper function exists, call that
instead of regenerating the expression tree. Repeated calls to the helper function
shortcircuit the evaluation logic.
Author: Nong Li <nong@databricks.com>
Author: Nong Li <nongli@gmail.com>
This patch had conflicts when merged, resolved by
Committer: Michael Armbrust <michael@databricks.com>
Closes#9480 from nongli/spark-10371.
Currently the user facing api for typed aggregation has some limitations:
* the customized typed aggregation must be the first of aggregation list
* the customized typed aggregation can only use long as buffer type
* the customized typed aggregation can only use flat type as result type
This PR tries to remove these limitations.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9599 from cloud-fan/agg.
https://issues.apache.org/jira/browse/SPARK-9830
This PR contains the following main changes.
* Removing `AggregateExpression1`.
* Removing `Aggregate` operator, which is used to evaluate `AggregateExpression1`.
* Removing planner rule used to plan `Aggregate`.
* Linking `MultipleDistinctRewriter` to analyzer.
* Renaming `AggregateExpression2` to `AggregateExpression` and `AggregateFunction2` to `AggregateFunction`.
* Updating places where we create aggregate expression. The way to create aggregate expressions is `AggregateExpression(aggregateFunction, mode, isDistinct)`.
* Changing `val`s in `DeclarativeAggregate`s that touch children of this function to `lazy val`s (when we create aggregate expression in DataFrame API, children of an aggregate function can be unresolved).
Author: Yin Huai <yhuai@databricks.com>
Closes#9556 from yhuai/removeAgg1.
A few changes:
1. Removed fold, since it can be confusing for distributed collections.
2. Created specific interfaces for each Dataset function (e.g. MapFunction, ReduceFunction, MapPartitionsFunction)
3. Added more documentation and test cases.
The other thing I'm considering doing is to have a "collector" interface for FlatMapFunction and MapPartitionsFunction, similar to MapReduce's map function.
Author: Reynold Xin <rxin@databricks.com>
Closes#9531 from rxin/SPARK-11564.
This PR adds support for multiple column in a single count distinct aggregate to the new aggregation path.
cc yhuai
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#9409 from hvanhovell/SPARK-11451.
This PR is a follow up for PR https://github.com/apache/spark/pull/9406. It adds more documentation to the rewriting rule, removes a redundant if expression in the non-distinct aggregation path and adds a multiple distinct test to the AggregationQuerySuite.
cc yhuai marmbrus
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#9541 from hvanhovell/SPARK-9241-followup.
The second PR for SPARK-9241, this adds support for multiple distinct columns to the new aggregation code path.
This PR solves the multiple DISTINCT column problem by rewriting these Aggregates into an Expand-Aggregate-Aggregate combination. See the [JIRA ticket](https://issues.apache.org/jira/browse/SPARK-9241) for some information on this. The advantages over the - competing - [first PR](https://github.com/apache/spark/pull/9280) are:
- This can use the faster TungstenAggregate code path.
- It is impossible to OOM due to an ```OpenHashSet``` allocating to much memory. However, this will multiply the number of input rows by the number of distinct clauses (plus one), and puts a lot more memory pressure on the aggregation code path itself.
The location of this Rule is a bit funny, and should probably change when the old aggregation path is changed.
cc yhuai - Could you also tell me where to add tests for this?
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#9406 from hvanhovell/SPARK-9241-rewriter.
This PR enables the Expand operator to process and produce Unsafe Rows.
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#9414 from hvanhovell/SPARK-11450.
https://issues.apache.org/jira/browse/SPARK-10116
This is really trivial, just happened to notice it -- if `XORShiftRandom.hashSeed` is really supposed to have random bits throughout (as the comment implies), it needs to do something for the conversion to `long`.
mengxr mkolod
Author: Imran Rashid <irashid@cloudera.com>
Closes#8314 from squito/SPARK-10116.
JIRA: https://issues.apache.org/jira/browse/SPARK-9162
Currently ScalaUDF extends CodegenFallback and doesn't provide code generation implementation. This path implements code generation for ScalaUDF.
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#9270 from viirya/scalaudf-codegen.
A cleanup for https://github.com/apache/spark/pull/9085.
The `DecimalLit` is very similar to `FloatLit`, we can just keep one of them.
Also added low level unit test at `SqlParserSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9482 from cloud-fan/parser.
This PR adds the ability to do typed SQL aggregations. We will likely also want to provide an interface to allow users to do aggregations on objects, but this is deferred to another PR.
```scala
val ds = Seq(("a", 10), ("a", 20), ("b", 1), ("b", 2), ("c", 1)).toDS()
ds.groupBy(_._1).agg(sum("_2").as[Int]).collect()
res0: Array(("a", 30), ("b", 3), ("c", 1))
```
Author: Michael Armbrust <michael@databricks.com>
Closes#9499 from marmbrus/dataset-agg.
Currently, if the Timestamp is before epoch (1970/01/01), the hours, minutes and seconds will be negative (also rounding up).
Author: Davies Liu <davies@databricks.com>
Closes#9502 from davies/neg_hour.
functions.scala was getting pretty long. I broke it into multiple files.
I also added explicit data types for some public vals, and renamed aggregate function pretty names to lower case, which is more consistent with rest of the functions.
Author: Reynold Xin <rxin@databricks.com>
Closes#9471 from rxin/SPARK-11505.
stddev is an alias for stddev_samp. variance should be consistent with stddev.
Also took the chance to remove internal Stddev and Variance, and only kept StddevSamp/StddevPop and VarianceSamp/VariancePop.
Author: Reynold Xin <rxin@databricks.com>
Closes#9449 from rxin/SPARK-11490.
Right now, SQL's mutable projection updates every value of the mutable project after it evaluates the corresponding expression. This makes the behavior of MutableProjection confusing and complicate the implementation of common aggregate functions like stddev because developers need to be aware that when evaluating {{i+1}}th expression of a mutable projection, {{i}}th slot of the mutable row has already been updated.
This PR make the MutableProjection atomic, by generating all the results of expressions first, then copy them into mutableRow.
Had run a mircro-benchmark, there is no notable performance difference between using class members and local variables.
cc yhuai
Author: Davies Liu <davies@databricks.com>
Closes#9422 from davies/atomic_mutable and squashes the following commits:
bbc1758 [Davies Liu] support wide table
8a0ae14 [Davies Liu] fix bug
bec07da [Davies Liu] refactor
2891628 [Davies Liu] make mutableProjection atomic
Hive GenericUDTF#initialize() defines field names in a returned schema though,
the current HiveGenericUDTF drops these names.
We might need to reflect these in a logical plan tree.
Author: navis.ryu <navis@apache.org>
Closes#8456 from navis/SPARK-9034.
1. Supporting expanding structs in Projections. i.e.
"SELECT s.*" where s is a struct type.
This is fixed by allowing the expand function to handle structs in addition to tables.
2. Supporting expanding * inside aggregate functions of structs.
"SELECT max(struct(col1, structCol.*))"
This requires recursively expanding the expressions. In this case, it it the aggregate
expression "max(...)" and we need to recursively expand its children inputs.
Author: Nong Li <nongli@gmail.com>
Closes#9343 from nongli/spark-11329.
From Reynold in the thread 'Exception when using some aggregate operators' (http://search-hadoop.com/m/q3RTt0xFr22nXB4/):
I don't think these are bugs. The SQL standard for average is "avg", not "mean". Similarly, a distinct count is supposed to be written as "count(distinct col)", not "countDistinct(col)".
We can, however, make "mean" an alias for "avg" to improve compatibility between DataFrame and SQL.
Author: tedyu <yuzhihong@gmail.com>
Closes#9332 from ted-yu/master.
JIRA: https://issues.apache.org/jira/browse/SPARK-9298
This patch adds pearson correlation aggregation function based on `AggregateExpression2`.
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#8587 from viirya/corr_aggregation.
DISTRIBUTE BY allows the user to hash partition the data by specified exprs. It also allows for
optioning sorting within each resulting partition. There is no required relationship between the
exprs for partitioning and sorting (i.e. one does not need to be a prefix of the other).
This patch adds to APIs to DataFrames which can be used together to provide this functionality:
1. distributeBy() which partitions the data frame into a specified number of partitions using the
partitioning exprs.
2. localSort() which sorts each partition using the provided sorting exprs.
To get the DISTRIBUTE BY functionality, the user simply does: df.distributeBy(...).localSort(...)
Author: Nong Li <nongli@gmail.com>
Closes#9364 from nongli/spark-11410.
Add a rule in optimizer to convert NULL [NOT] IN (expr1,...,expr2) to
Literal(null).
This is a follow up defect to SPARK-8654
cloud-fan Can you please take a look ?
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#9348 from dilipbiswal/spark_11024.
Older version of Janino (>2.7) does not support Override, we should not use that in codegen.
Author: Davies Liu <davies@databricks.com>
Closes#9372 from davies/no_override.
This PR introduce a mechanism to call spill() on those SQL operators that support spilling (for example, BytesToBytesMap, UnsafeExternalSorter and ShuffleExternalSorter) if there is not enough memory for execution. The preserved first page is needed anymore, so removed.
Other Spillable objects in Spark core (ExternalSorter and AppendOnlyMap) are not included in this PR, but those could benefit from this (trigger others' spilling).
The PrepareRDD may be not needed anymore, could be removed in follow up PR.
The following script will fail with OOM before this PR, finished in 150 seconds with 2G heap (also works in 1.5 branch, with similar duration).
```python
sqlContext.setConf("spark.sql.shuffle.partitions", "1")
df = sqlContext.range(1<<25).selectExpr("id", "repeat(id, 2) as s")
df2 = df.select(df.id.alias('id2'), df.s.alias('s2'))
j = df.join(df2, df.id==df2.id2).groupBy(df.id).max("id", "id2")
j.explain()
print j.count()
```
For thread-safety, here what I'm got:
1) Without calling spill(), the operators should only be used by single thread, no safety problems.
2) spill() could be triggered in two cases, triggered by itself, or by other operators. we can check trigger == this in spill(), so it's still in the same thread, so safety problems.
3) if it's triggered by other operators (right now cache will not trigger spill()), we only spill the data into disk when it's in scanning stage (building is finished), so the in-memory sorter or memory pages are read-only, we only need to synchronize the iterator and change it.
4) During scanning, the iterator will only use one record in one page, we can't free this page, because the downstream is currently using it (used by UnsafeRow or other objects). In BytesToBytesMap, we just skip the current page, and dump all others into disk. In UnsafeExternalSorter, we keep the page that is used by current record (having the same baseObject), free it when loading the next record. In ShuffleExternalSorter, the spill() will not trigger during scanning.
5) In order to avoid deadlock, we didn't call acquireMemory during spill (so we reused the pointer array in InMemorySorter).
Author: Davies Liu <davies@databricks.com>
Closes#9241 from davies/force_spill.
This is minor, but I ran into while writing Datasets and while it wasn't needed for the final solution, it was super confusing so we should fix it.
Basically we recurse into `Seq` to see if they have children. This breaks because we don't preserve the original subclass of `Seq` (and `StructType <:< Seq[StructField]`). Since a struct can never contain children, lets just not recurse into it.
Author: Michael Armbrust <michael@databricks.com>
Closes#9334 from marmbrus/structMakeCopy.
This PR adds a new operation `joinWith` to a `Dataset`, which returns a `Tuple` for each pair where a given `condition` evaluates to true.
```scala
case class ClassData(a: String, b: Int)
val ds1 = Seq(ClassData("a", 1), ClassData("b", 2)).toDS()
val ds2 = Seq(("a", 1), ("b", 2)).toDS()
> ds1.joinWith(ds2, $"_1" === $"a").collect()
res0: Array((ClassData("a", 1), ("a", 1)), (ClassData("b", 2), ("b", 2)))
```
This operation is similar to the relation `join` function with one important difference in the result schema. Since `joinWith` preserves objects present on either side of the join, the result schema is similarly nested into a tuple under the column names `_1` and `_2`.
This type of join can be useful both for preserving type-safety with the original object types as well as working with relational data where either side of the join has column names in common.
## Required Changes to Encoders
In the process of working on this patch, several deficiencies to the way that we were handling encoders were discovered. Specifically, it turned out to be very difficult to `rebind` the non-expression based encoders to extract the nested objects from the results of joins (and also typed selects that return tuples).
As a result the following changes were made.
- `ClassEncoder` has been renamed to `ExpressionEncoder` and has been improved to also handle primitive types. Additionally, it is now possible to take arbitrary expression encoders and rewrite them into a single encoder that returns a tuple.
- All internal operations on `Dataset`s now require an `ExpressionEncoder`. If the users tries to pass a non-`ExpressionEncoder` in, an error will be thrown. We can relax this requirement in the future by constructing a wrapper class that uses expressions to project the row to the expected schema, shielding the users code from the required remapping. This will give us a nice balance where we don't force user encoders to understand attribute references and binding, but still allow our native encoder to leverage runtime code generation to construct specific encoders for a given schema that avoid an extra remapping step.
- Additionally, the semantics for different types of objects are now better defined. As stated in the `ExpressionEncoder` scaladoc:
- Classes will have their sub fields extracted by name using `UnresolvedAttribute` expressions
and `UnresolvedExtractValue` expressions.
- Tuples will have their subfields extracted by position using `BoundReference` expressions.
- Primitives will have their values extracted from the first ordinal with a schema that defaults
to the name `value`.
- Finally, the binding lifecycle for `Encoders` has now been unified across the codebase. Encoders are now `resolved` to the appropriate schema in the constructor of `Dataset`. This process replaces an unresolved expressions with concrete `AttributeReference` expressions. Binding then happens on demand, when an encoder is going to be used to construct an object. This closely mirrors the lifecycle for standard expressions when executing normal SQL or `DataFrame` queries.
Author: Michael Armbrust <michael@databricks.com>
Closes#9300 from marmbrus/datasets-tuples.
When sampling and then filtering DataFrame, the SQL Optimizer will push down filter into sample and produce wrong result. This is due to the sampler is calculated based on the original scope rather than the scope after filtering.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#9294 from yanboliang/spark-11303.
I'm new to spark. I was trying out the sort_array function then hit this exception. I looked into the spark source code. I found the root cause is that sort_array does not check for an array of NULLs. It's not meaningful to sort an array of entirely NULLs anyway.
I'm adding a check on the input array type to SortArray. If the array consists of NULLs entirely, there is no need to sort such array. I have also added a test case for this.
Please help to review my fix. Thanks!
Author: Jia Li <jiali@us.ibm.com>
Closes#9247 from jliwork/SPARK-11277.
This patch refactors the MemoryManager class structure. After #9000, Spark had the following classes:
- MemoryManager
- StaticMemoryManager
- ExecutorMemoryManager
- TaskMemoryManager
- ShuffleMemoryManager
This is fairly confusing. To simplify things, this patch consolidates several of these classes:
- ShuffleMemoryManager and ExecutorMemoryManager were merged into MemoryManager.
- TaskMemoryManager is moved into Spark Core.
**Key changes and tasks**:
- [x] Merge ExecutorMemoryManager into MemoryManager.
- [x] Move pooling logic into Allocator.
- [x] Move TaskMemoryManager from `spark-unsafe` to `spark-core`.
- [x] Refactor the existing Tungsten TaskMemoryManager interactions so Tungsten code use only this and not both this and ShuffleMemoryManager.
- [x] Refactor non-Tungsten code to use the TaskMemoryManager instead of ShuffleMemoryManager.
- [x] Merge ShuffleMemoryManager into MemoryManager.
- [x] Move code
- [x] ~~Simplify 1/n calculation.~~ **Will defer to followup, since this needs more work.**
- [x] Port ShuffleMemoryManagerSuite tests.
- [x] Move classes from `unsafe` package to `memory` package.
- [ ] Figure out how to handle the hacky use of the memory managers in HashedRelation's broadcast variable construction.
- [x] Test porting and cleanup: several tests relied on mock functionality (such as `TestShuffleMemoryManager.markAsOutOfMemory`) which has been changed or broken during the memory manager consolidation
- [x] AbstractBytesToBytesMapSuite
- [x] UnsafeExternalSorterSuite
- [x] UnsafeFixedWidthAggregationMapSuite
- [x] UnsafeKVExternalSorterSuite
**Compatiblity notes**:
- This patch introduces breaking changes in `ExternalAppendOnlyMap`, which is marked as `DevloperAPI` (likely for legacy reasons): this class now cannot be used outside of a task.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#9127 from JoshRosen/SPARK-10984.
marmbrus rxin I believe these typecasts are not required in the presence of explicit return types.
Author: Alexander Slesarenko <avslesarenko@gmail.com>
Closes#9262 from aslesarenko/remove-typecasts.
For nested StructType, the underline buffer could be used for others before, we should zero out the padding bytes for those primitive types that have less than 8 bytes.
cc cloud-fan
Author: Davies Liu <davies@databricks.com>
Closes#9217 from davies/zero_out.
*This PR adds a new experimental API to Spark, tentitively named Datasets.*
A `Dataset` is a strongly-typed collection of objects that can be transformed in parallel using functional or relational operations. Example usage is as follows:
### Functional
```scala
> val ds: Dataset[Int] = Seq(1, 2, 3).toDS()
> ds.filter(_ % 1 == 0).collect()
res1: Array[Int] = Array(1, 2, 3)
```
### Relational
```scala
scala> ds.toDF().show()
+-----+
|value|
+-----+
| 1|
| 2|
| 3|
+-----+
> ds.select(expr("value + 1").as[Int]).collect()
res11: Array[Int] = Array(2, 3, 4)
```
## Comparison to RDDs
A `Dataset` differs from an `RDD` in the following ways:
- The creation of a `Dataset` requires the presence of an explicit `Encoder` that can be
used to serialize the object into a binary format. Encoders are also capable of mapping the
schema of a given object to the Spark SQL type system. In contrast, RDDs rely on runtime
reflection based serialization.
- Internally, a `Dataset` is represented by a Catalyst logical plan and the data is stored
in the encoded form. This representation allows for additional logical operations and
enables many operations (sorting, shuffling, etc.) to be performed without deserializing to
an object.
A `Dataset` can be converted to an `RDD` by calling the `.rdd` method.
## Comparison to DataFrames
A `Dataset` can be thought of as a specialized DataFrame, where the elements map to a specific
JVM object type, instead of to a generic `Row` container. A DataFrame can be transformed into
specific Dataset by calling `df.as[ElementType]`. Similarly you can transform a strongly-typed
`Dataset` to a generic DataFrame by calling `ds.toDF()`.
## Implementation Status and TODOs
This is a rough cut at the least controversial parts of the API. The primary purpose here is to get something committed so that we can better parallelize further work and get early feedback on the API. The following is being deferred to future PRs:
- Joins and Aggregations (prototype here f11f91e6f0)
- Support for Java
Additionally, the responsibility for binding an encoder to a given schema is currently done in a fairly ad-hoc fashion. This is an internal detail, and what we are doing today works for the cases we care about. However, as we add more APIs we'll probably need to do this in a more principled way (i.e. separate resolution from binding as we do in DataFrames).
## COMPATIBILITY NOTE
Long term we plan to make `DataFrame` extend `Dataset[Row]`. However,
making this change to che class hierarchy would break the function signatures for the existing
function operations (map, flatMap, etc). As such, this class should be considered a preview
of the final API. Changes will be made to the interface after Spark 1.6.
Author: Michael Armbrust <michael@databricks.com>
Closes#9190 from marmbrus/dataset-infra.
This PR change InMemoryTableScan to output UnsafeRow, and optimize the unrolling and scanning by coping the bytes for var-length types between UnsafeRow and ByteBuffer directly without creating the wrapper objects. When scanning the decimals in TPC-DS store_sales table, it's 80% faster (copy it as long without create Decimal objects).
Author: Davies Liu <davies@databricks.com>
Closes#9203 from davies/unsafe_cache.
In the analysis phase , while processing the rules for IN predicate, we
compare the in-list types to the lhs expression type and generate
cast operation if necessary. In the case of NULL [NOT] IN expr1 , we end up
generating cast between in list types to NULL like cast (1 as NULL) which
is not a valid cast.
The fix is to find a common type between LHS and RHS expressions and cast
all the expression to the common type.
Author: Dilip Biswal <dbiswal@us.ibm.com>
This patch had conflicts when merged, resolved by
Committer: Michael Armbrust <michael@databricks.com>
Closes#9036 from dilipbiswal/spark_8654_new.
I am changing the default behavior of `First`/`Last` to respect null values (the SQL standard default behavior).
https://issues.apache.org/jira/browse/SPARK-9740
Author: Yin Huai <yhuai@databricks.com>
Closes#8113 from yhuai/firstLast.
This PR introduce a new feature to run SQL directly on files without create a table, for example:
```
select id from json.`path/to/json/files` as j
```
Author: Davies Liu <davies@databricks.com>
Closes#9173 from davies/source.
Find out the missing attributes by recursively looking
at the sort order expression and rest of the code
takes care of projecting them out.
Added description from cloud-fan
I wanna explain a bit more about this bug.
When we resolve sort ordering, we will use a special method, which only resolves UnresolvedAttributes and UnresolvedExtractValue. However, for something like Floor('a), even the 'a is resolved, the floor expression may still being unresolved as data type mismatch(for example, 'a is string type and Floor need double type), thus can't pass this filter, and we can't push down this missing attribute 'a
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#9123 from dilipbiswal/SPARK-10534.
Implement encode/decode for external row based on `ClassEncoder`.
TODO:
* code cleanup
* ~~fix corner cases~~
* refactor the encoder interface
* improve test for product codegen, to cover more corner cases.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9184 from cloud-fan/encoder.
Push conjunctive predicates though Aggregate operators when their references are a subset of the groupingExpressions.
Query plan before optimisation :-
Filter ((c#138L = 2) && (a#0 = 3))
Aggregate [a#0], [a#0,count(b#1) AS c#138L]
Project [a#0,b#1]
LocalRelation [a#0,b#1,c#2]
Query plan after optimisation :-
Filter (c#138L = 2)
Aggregate [a#0], [a#0,count(b#1) AS c#138L]
Filter (a#0 = 3)
Project [a#0,b#1]
LocalRelation [a#0,b#1,c#2]
Author: nitin goyal <nitin.goyal@guavus.com>
Author: nitin.goyal <nitin.goyal@guavus.com>
Closes#9167 from nitin2goyal/master.
This PR improve the performance by:
1) Generate an Iterator that take Iterator[CachedBatch] as input, and call accessors (unroll the loop for columns), avoid the expensive Iterator.flatMap.
2) Use Unsafe.getInt/getLong/getFloat/getDouble instead of ByteBuffer.getInt/getLong/getFloat/getDouble, the later one actually read byte by byte.
3) Remove the unnecessary copy() in Coalesce(), which is not related to memory cache, found during benchmark.
The following benchmark showed that we can speedup the columnar cache of int by 2x.
```
path = '/opt/tpcds/store_sales/'
int_cols = ['ss_sold_date_sk', 'ss_sold_time_sk', 'ss_item_sk','ss_customer_sk']
df = sqlContext.read.parquet(path).select(int_cols).cache()
df.count()
t = time.time()
print df.select("*")._jdf.queryExecution().toRdd().count()
print time.time() - t
```
Author: Davies Liu <davies@databricks.com>
Closes#9145 from davies/byte_buffer.
Currently, we use CartesianProduct for join with null-safe-equal condition.
```
scala> sqlContext.sql("select * from t a join t b on (a.i <=> b.i)").explain
== Physical Plan ==
TungstenProject [i#2,j#3,i#7,j#8]
Filter (i#2 <=> i#7)
CartesianProduct
LocalTableScan [i#2,j#3], [[1,1]]
LocalTableScan [i#7,j#8], [[1,1]]
```
Actually, we can have an equal-join condition as `coalesce(i, default) = coalesce(b.i, default)`, then an partitioned join algorithm could be used.
After this PR, the plan will become:
```
>>> sqlContext.sql("select * from a join b ON a.id <=> b.id").explain()
TungstenProject [id#0L,id#1L]
Filter (id#0L <=> id#1L)
SortMergeJoin [coalesce(id#0L,0)], [coalesce(id#1L,0)]
TungstenSort [coalesce(id#0L,0) ASC], false, 0
TungstenExchange hashpartitioning(coalesce(id#0L,0),200)
ConvertToUnsafe
Scan PhysicalRDD[id#0L]
TungstenSort [coalesce(id#1L,0) ASC], false, 0
TungstenExchange hashpartitioning(coalesce(id#1L,0),200)
ConvertToUnsafe
Scan PhysicalRDD[id#1L]
```
Author: Davies Liu <davies@databricks.com>
Closes#9120 from davies/null_safe.
We can't parse `NOT` operator with comparison operations like `SELECT NOT TRUE > TRUE`, this PR fixed it.
Takes over https://github.com/apache/spark/pull/6326.
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#8617 from cloud-fan/not.
The purpose of this PR is to keep the unsafe format detail only inside the unsafe class itself, so when we use them(like use unsafe array in unsafe map, use unsafe array and map in columnar cache), we don't need to understand the format before use them.
change list:
* unsafe array's 4-bytes numElements header is now required(was optional), and become a part of unsafe array format.
* w.r.t the previous changing, the `sizeInBytes` of unsafe array now counts the 4-bytes header.
* unsafe map's format was `[numElements] [key array numBytes] [key array content(without numElements header)] [value array content(without numElements header)]` before, which is a little hacky as it makes unsafe array's header optional. I think saving 4 bytes is not a big deal, so the format is now: `[key array numBytes] [unsafe key array] [unsafe value array]`.
* w.r.t the previous changing, the `sizeInBytes` of unsafe map now counts both map's header and array's header.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9131 from cloud-fan/unsafe.
Some json parsers are not closed. parser in JacksonParser#parseJson, for example.
Author: navis.ryu <navis@apache.org>
Closes#9130 from navis/SPARK-11124.
Actually all of the `UnaryMathExpression` doens't support the Decimal, will create follow ups for supporing it. This is the first PR which will be good to review the approach I am taking.
Author: Cheng Hao <hao.cheng@intel.com>
Closes#9086 from chenghao-intel/ceiling.
This patch extends TungstenAggregate to support ImperativeAggregate functions. The existing TungstenAggregate operator only supported DeclarativeAggregate functions, which are defined in terms of Catalyst expressions and can be evaluated via generated projections. ImperativeAggregate functions, on the other hand, are evaluated by calling their `initialize`, `update`, `merge`, and `eval` methods.
The basic strategy here is similar to how SortBasedAggregate evaluates both types of aggregate functions: use a generated projection to evaluate the expression-based declarative aggregates with dummy placeholder expressions inserted in place of the imperative aggregate function output, then invoke the imperative aggregate functions and target them against the aggregation buffer. The bulk of the diff here consists of code that was copied and adapted from SortBasedAggregate, with some key changes to handle TungstenAggregate's sort fallback path.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#9038 from JoshRosen/support-interpreted-in-tungsten-agg-final.
Right now, we have QualifiedTableName, TableIdentifier, and Seq[String] to represent table identifiers. We should only have one form and TableIdentifier is the best one because it provides methods to get table name, database name, return unquoted string, and return quoted string.
Author: Wenchen Fan <wenchen@databricks.com>
Author: Wenchen Fan <cloud0fan@163.com>
Closes#8453 from cloud-fan/table-name.
We should not stop resolving having when the having condtion is resolved, or something like `count(1)` will crash.
Author: Wenchen Fan <cloud0fan@163.com>
Closes#9105 from cloud-fan/having.
This is a first draft of the ability to construct expressions that will take a catalyst internal row and construct a Product (case class or tuple) that has fields with the correct names. Support include:
- Nested classes
- Maps
- Efficiently handling of arrays of primitive types
Not yet supported:
- Case classes that require custom collection types (i.e. List instead of Seq).
Author: Michael Armbrust <michael@databricks.com>
Closes#9100 from marmbrus/productContructor.
In the current implementation of named expressions' `ExprIds`, we rely on a per-JVM AtomicLong to ensure that expression ids are unique within a JVM. However, these expression ids will not be _globally_ unique. This opens the potential for id collisions if new expression ids happen to be created inside of tasks rather than on the driver.
There are currently a few cases where tasks allocate expression ids, which happen to be safe because those expressions are never compared to expressions created on the driver. In order to guard against the introduction of invalid comparisons between driver-created and executor-created expression ids, this patch extends `ExprId` to incorporate a UUID to identify the JVM that created the id, which prevents collisions.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#9093 from JoshRosen/SPARK-11080.
This PR improve the unrolling and read of complex types in columnar cache:
1) Using UnsafeProjection to do serialization of complex types, so they will not be serialized three times (two for actualSize)
2) Copy the bytes from UnsafeRow/UnsafeArrayData to ByteBuffer directly, avoiding the immediate byte[]
3) Using the underlying array in ByteBuffer to create UTF8String/UnsafeRow/UnsafeArrayData without copy.
Combine these optimizations, we can reduce the unrolling time from 25s to 21s (20% less), reduce the scanning time from 3.5s to 2.5s (28% less).
```
df = sqlContext.read.parquet(path)
t = time.time()
df.cache()
df.count()
print 'unrolling', time.time() - t
for i in range(10):
t = time.time()
print df.select("*")._jdf.queryExecution().toRdd().count()
print time.time() - t
```
The schema is
```
root
|-- a: struct (nullable = true)
| |-- b: long (nullable = true)
| |-- c: string (nullable = true)
|-- d: array (nullable = true)
| |-- element: long (containsNull = true)
|-- e: map (nullable = true)
| |-- key: long
| |-- value: string (valueContainsNull = true)
```
Now the columnar cache depends on that UnsafeProjection support all the data types (including UDT), this PR also fix that.
Author: Davies Liu <davies@databricks.com>
Closes#9016 from davies/complex2.
JIRA: https://issues.apache.org/jira/browse/SPARK-10960
When accessing a column in inner select from a select with window function, `AnalysisException` will be thrown. For example, an query like this:
select area, rank() over (partition by area order by tmp.month) + tmp.tmp1 as c1 from (select month, area, product, 1 as tmp1 from windowData) tmp
Currently, the rule `ExtractWindowExpressions` in `Analyzer` only extracts regular expressions from `WindowFunction`, `WindowSpecDefinition` and `AggregateExpression`. We need to also extract other attributes as the one in `Alias` as shown in the above query.
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#9011 from viirya/fix-window-inner-column.
This PR improve the sessions management by replacing the thread-local based to one SQLContext per session approach, introduce separated temporary tables and UDFs/UDAFs for each session.
A new session of SQLContext could be created by:
1) create an new SQLContext
2) call newSession() on existing SQLContext
For HiveContext, in order to reduce the cost for each session, the classloader and Hive client are shared across multiple sessions (created by newSession).
CacheManager is also shared by multiple sessions, so cache a table multiple times in different sessions will not cause multiple copies of in-memory cache.
Added jars are still shared by all the sessions, because SparkContext does not support sessions.
cc marmbrus yhuai rxin
Author: Davies Liu <davies@databricks.com>
Closes#8909 from davies/sessions.
UnsafeRow contains 3 pieces of information when pointing to some data in memory (an object, a base offset, and length). When the row is serialized with Java/Kryo serialization, the object layout in memory can change if two machines have different pointer width (Oops in JVM).
To reproduce, launch Spark using
MASTER=local-cluster[2,1,1024] bin/spark-shell --conf "spark.executor.extraJavaOptions=-XX:-UseCompressedOops"
And then run the following
scala> sql("select 1 xx").collect()
Author: Reynold Xin <rxin@databricks.com>
Closes#9030 from rxin/SPARK-10914.
This PR refactors Parquet write path to follow parquet-format spec. It's a successor of PR #7679, but with less non-essential changes.
Major changes include:
1. Replaces `RowWriteSupport` and `MutableRowWriteSupport` with `CatalystWriteSupport`
- Writes Parquet data using standard layout defined in parquet-format
Specifically, we are now writing ...
- ... arrays and maps in standard 3-level structure with proper annotations and field names
- ... decimals as `INT32` and `INT64` whenever possible, and taking `FIXED_LEN_BYTE_ARRAY` as the final fallback
- Supports legacy mode which is compatible with Spark 1.4 and prior versions
The legacy mode is by default off, and can be turned on by flipping SQL option `spark.sql.parquet.writeLegacyFormat` to `true`.
- Eliminates per value data type dispatching costs via prebuilt composed writer functions
1. Cleans up the last pieces of old Parquet support code
As pointed out by rxin previously, we probably want to rename all those `Catalyst*` Parquet classes to `Parquet*` for clarity. But I'd like to do this in a follow-up PR to minimize code review noises in this one.
Author: Cheng Lian <lian@databricks.com>
Closes#8988 from liancheng/spark-8848/standard-parquet-write-path.
This PR is a first cut at code generating an encoder that takes a Scala `Product` type and converts it directly into the tungsten binary format. This is done through the addition of a new set of expression that can be used to invoke methods on raw JVM objects, extracting fields and converting the result into the required format. These can then be used directly in an `UnsafeProjection` allowing us to leverage the existing encoding logic.
According to some simple benchmarks, this can significantly speed up conversion (~4x). However, replacing CatalystConverters is deferred to a later PR to keep this PR at a reasonable size.
```scala
case class SomeInts(a: Int, b: Int, c: Int, d: Int, e: Int)
val data = SomeInts(1, 2, 3, 4, 5)
val encoder = ProductEncoder[SomeInts]
val converter = CatalystTypeConverters.createToCatalystConverter(ScalaReflection.schemaFor[SomeInts].dataType)
(1 to 5).foreach {iter =>
benchmark(s"converter $iter") {
var i = 100000000
while (i > 0) {
val res = converter(data).asInstanceOf[InternalRow]
assert(res.getInt(0) == 1)
assert(res.getInt(1) == 2)
i -= 1
}
}
benchmark(s"encoder $iter") {
var i = 100000000
while (i > 0) {
val res = encoder.toRow(data)
assert(res.getInt(0) == 1)
assert(res.getInt(1) == 2)
i -= 1
}
}
}
```
Results:
```
[info] converter 1: 7170ms
[info] encoder 1: 1888ms
[info] converter 2: 6763ms
[info] encoder 2: 1824ms
[info] converter 3: 6912ms
[info] encoder 3: 1802ms
[info] converter 4: 7131ms
[info] encoder 4: 1798ms
[info] converter 5: 7350ms
[info] encoder 5: 1912ms
```
Author: Michael Armbrust <michael@databricks.com>
Closes#9019 from marmbrus/productEncoder.
This PR refactors `HashJoinNode` to take a existing `HashedRelation`. So, we can reuse this node for both `ShuffledHashJoin` and `BroadcastHashJoin`.
https://issues.apache.org/jira/browse/SPARK-10887
Author: Yin Huai <yhuai@databricks.com>
Closes#8953 from yhuai/SPARK-10887.
In the analysis phase , while processing the rules for IN predicate, we
compare the in-list types to the lhs expression type and generate
cast operation if necessary. In the case of NULL [NOT] IN expr1 , we end up
generating cast between in list types to NULL like cast (1 as NULL) which
is not a valid cast.
The fix is to not generate such a cast if the lhs type is a NullType instead
we translate the expression to Literal(Null).
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#8983 from dilipbiswal/spark_8654.
Its pretty hard to debug problems with expressions when you can't see all the arguments.
Before: `invoke()`
After: `invoke(inputObject#1, intField, IntegerType)`
Author: Michael Armbrust <michael@databricks.com>
Closes#9022 from marmbrus/expressionToString.
This PR improve the performance of complex types in columnar cache by using UnsafeProjection instead of KryoSerializer.
A simple benchmark show that this PR could improve the performance of scanning a cached table with complex columns by 15x (comparing to Spark 1.5).
Here is the code used to benchmark:
```
df = sc.range(1<<23).map(lambda i: Row(a=Row(b=i, c=str(i)), d=range(10), e=dict(zip(range(10), [str(i) for i in range(10)])))).toDF()
df.write.parquet("table")
```
```
df = sqlContext.read.parquet("table")
df.cache()
df.count()
t = time.time()
print df.select("*")._jdf.queryExecution().toRdd().count()
print time.time() - t
```
Author: Davies Liu <davies@databricks.com>
Closes#8971 from davies/complex.
This patch allows `Repartition` to support UnsafeRows. This is accomplished by implementing the logical `Repartition` operator in terms of `Exchange` and a new `RoundRobinPartitioning`.
Author: Josh Rosen <joshrosen@databricks.com>
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#8083 from JoshRosen/SPARK-9702.
The created decimal is wrong if using `Decimal(unscaled, precision, scale)` with unscaled > 1e18 and and precision > 18 and scale > 0.
This bug exists since the beginning.
Author: Davies Liu <davies@databricks.com>
Closes#9014 from davies/fix_decimal.
DeclarativeAggregate matches more closely with ImperativeAggregate we already have.
Author: Reynold Xin <rxin@databricks.com>
Closes#9013 from rxin/SPARK-10982.
This patch refactors several of the Aggregate2 interfaces in order to improve code clarity.
The biggest change is a refactoring of the `AggregateFunction2` class hierarchy. In the old code, we had a class named `AlgebraicAggregate` that inherited from `AggregateFunction2`, added a new set of methods, then banned the use of the inherited methods. I found this to be fairly confusing because.
If you look carefully at the existing code, you'll see that subclasses of `AggregateFunction2` fall into two disjoint categories: imperative aggregation functions which directly extended `AggregateFunction2` and declarative, expression-based aggregate functions which extended `AlgebraicAggregate`. In order to make this more explicit, this patch refactors things so that `AggregateFunction2` is a sealed abstract class with two subclasses, `ImperativeAggregateFunction` and `ExpressionAggregateFunction`. The superclass, `AggregateFunction2`, now only contains methods and fields that are common to both subclasses.
After making this change, I updated the various AggregationIterator classes to comply with this new naming scheme. I also performed several small renamings in the aggregate interfaces themselves in order to improve clarity and rewrote or expanded a number of comments.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#8973 from JoshRosen/tungsten-agg-comments.
This PR is mostly cosmetic and cleans up some warts in codegen (nearly all of which were inherited from the original quasiquote version).
- Add lines numbers to errors (in stacktraces when debug logging is on, and always for compile fails)
- Use a variable for input row instead of hardcoding "i" everywhere
- rename `primitive` -> `value` (since its often actually an object)
Author: Michael Armbrust <michael@databricks.com>
Closes#9006 from marmbrus/codegen-cleanup.
`Murmur3_x86_32.hashUnsafeWords` only accepts word-aligned bytes, but unsafe array is not.
Author: Wenchen Fan <cloud0fan@163.com>
Closes#8987 from cloud-fan/hash.
This PR is a completely rewritten of GenerateUnsafeProjection, to accomplish the goal of copying data only once. The old code of GenerateUnsafeProjection is still there to reduce review difficulty.
Instead of creating unsafe conversion code for struct, array and map, we create code of writing the content to the global row buffer.
Author: Wenchen Fan <cloud0fan@163.com>
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#8747 from cloud-fan/copy-once.
The utilities such as Substring#substringBinarySQL and BinaryPrefixComparator#computePrefix for binary data are put together in ByteArray for easy-to-read.
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Closes#8122 from maropu/CleanUpForBinaryType.
Floor & Ceiling function should returns Long type, rather than Double.
Verified with MySQL & Hive.
Author: Cheng Hao <hao.cheng@intel.com>
Closes#8933 from chenghao-intel/ceiling.
This is an implementation of Hive's `json_tuple` function using Jackson Streaming.
Author: Nathan Howell <nhowell@godaddy.com>
Closes#7946 from NathanHowell/SPARK-9617.
This PR implements a HyperLogLog based Approximate Count Distinct function using the new UDAF interface.
The implementation is inspired by the ClearSpring HyperLogLog implementation and should produce the same results.
There is still some documentation and testing left to do.
cc yhuai
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#8362 from hvanhovell/SPARK-9741.
When reading Parquet string and binary-backed decimal values, Parquet `Binary.getBytes` always returns a copied byte array, which is unnecessary. Since the underlying implementation of `Binary` values there is guaranteed to be `ByteArraySliceBackedBinary`, and Parquet itself never reuses underlying byte arrays, we can use `Binary.toByteBuffer.array()` to steal the underlying byte arrays without copying them.
This brings performance benefits when scanning Parquet string and binary-backed decimal columns. Note that, this trick doesn't cover binary-backed decimals with precision greater than 18.
My micro-benchmark result is that, this brings a ~15% performance boost for scanning TPC-DS `store_sales` table (scale factor 15).
Another minor optimization done in this PR is that, now we directly construct a Java `BigDecimal` in `Decimal.toJavaBigDecimal` without constructing a Scala `BigDecimal` first. This brings another ~5% performance gain.
Author: Cheng Lian <lian@databricks.com>
Closes#8907 from liancheng/spark-10811/eliminate-array-copying.
https://issues.apache.org/jira/browse/SPARK-10741
I choose the second approach: do not change output exprIds when convert MetastoreRelation to LogicalRelation
Author: Wenchen Fan <cloud0fan@163.com>
Closes#8889 from cloud-fan/hot-bug.
Since `scala.util.parsing.combinator.Parsers` is thread-safe since Scala 2.10 (See [SI-4929](https://issues.scala-lang.org/browse/SI-4929)), we can change SqlParser to object to avoid memory leak.
I didn't change other subclasses of `scala.util.parsing.combinator.Parsers` because there is only one instance in one SQLContext, which should not be an issue.
Author: zsxwing <zsxwing@gmail.com>
Closes#8357 from zsxwing/sql-memory-leak.
From JIRA: Schema merging should only handle struct fields. But currently we also reconcile decimal precision and scale information.
Author: Holden Karau <holden@pigscanfly.ca>
Closes#8634 from holdenk/SPARK-10449-dont-merge-different-precision.
Intersect and Except are both set operators and they use the all the columns to compare equality between rows. When pushing their Project parent down, the relations they based on would change, therefore not an equivalent transformation.
JIRA: https://issues.apache.org/jira/browse/SPARK-10539
I added some comments based on the fix of https://github.com/apache/spark/pull/8742.
Author: Yijie Shen <henry.yijieshen@gmail.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#8823 from yhuai/fix_set_optimization.
Kryo fails with buffer overflow even with max value (2G).
{noformat}
org.apache.spark.SparkException: Kryo serialization failed: Buffer overflow. Available: 0, required: 1
Serialization trace:
containsChild (org.apache.spark.sql.catalyst.expressions.BoundReference)
child (org.apache.spark.sql.catalyst.expressions.SortOrder)
array (scala.collection.mutable.ArraySeq)
ordering (org.apache.spark.sql.catalyst.expressions.InterpretedOrdering)
interpretedOrdering (org.apache.spark.sql.types.StructType)
schema (org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema). To avoid this, increase spark.kryoserializer.buffer.max value.
at org.apache.spark.serializer.KryoSerializerInstance.serialize(KryoSerializer.scala:263)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:240)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
{noformat}
Author: navis.ryu <navis@apache.org>
Closes#8808 from navis/SPARK-10684.
This fixes https://issues.apache.org/jira/browse/SPARK-9794 by using a real ISO8601 parser. (courtesy of the xml component of the standard java library)
cc: angelini
Author: Kevin Cox <kevincox@kevincox.ca>
Closes#8396 from kevincox/kevincox-sql-time-parsing.
Sometimes we can't push down the whole `Project` though `Sort`, but we still have a chance to push down part of it.
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#8644 from cloud-fan/column-prune.
JIRA: https://issues.apache.org/jira/browse/SPARK-10437
If an expression in `SortOrder` is a resolved one, such as `count(1)`, the corresponding rule in `Analyzer` to make it work in order by will not be applied.
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#8599 from viirya/orderby-agg.
Move .java files in `src/main/scala` to `src/main/java` root, except for `package-info.java` (to stay next to package.scala)
Author: Sean Owen <sowen@cloudera.com>
Closes#8736 from srowen/SPARK-10576.
Adding STDDEV support for DataFrame using 1-pass online /parallel algorithm to compute variance. Please review the code change.
Author: JihongMa <linlin200605@gmail.com>
Author: Jihong MA <linlin200605@gmail.com>
Author: Jihong MA <jihongma@jihongs-mbp.usca.ibm.com>
Author: Jihong MA <jihongma@Jihongs-MacBook-Pro.local>
Closes#6297 from JihongMA/SPARK-SQL.
Before this fix, `MyDenseVectorUDT.typeName` gives `mydensevecto`, which is not desirable.
Author: Cheng Lian <lian@databricks.com>
Closes#8640 from liancheng/spark-10472/udt-type-name.
Use these in the optimizer as well:
A and (not(A) or B) => A and B
not(A and B) => not(A) or not(B)
not(A or B) => not(A) and not(B)
Author: Yash Datta <Yash.Datta@guavus.com>
Closes#5700 from saucam/bool_simp.
The reason for this extra copy is that we iterate the array twice: calculate elements data size and copy elements to array buffer.
A simple solution is to follow `createCodeForStruct`, we can dynamically grow the buffer when needed and thus don't need to know the data size ahead.
This PR also include some typo and style fixes, and did some minor refactor to make sure `input.primitive` is always variable name not code when generate unsafe code.
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#8496 from cloud-fan/avoid-copy.
When we generate unsafe code inside `createCodeForXXX`, we always assign the `input.primitive` to a temp variable in case `input.primitive` is expression code.
This PR did some refactor to make sure `input.primitive` is always variable name, and some other typo and style fixes.
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#8613 from cloud-fan/minor.
The bulk of the changes are on `transient` annotation on class parameter. Often the compiler doesn't generate a field for this parameters, so the the transient annotation would be unnecessary.
But if the class parameter are used in methods, then fields are created. So it is safer to keep the annotations.
The remainder are some potential bugs, and deprecated syntax.
Author: Luc Bourlier <luc.bourlier@typesafe.com>
Closes#8433 from skyluc/issue/sbt-2.11.
We did a lot of special handling for non-deterministic expressions in `Optimizer`. However, `PhysicalOperation` just collects all Projects and Filters and mess it up. We should respect the operators order caused by non-deterministic expressions in `PhysicalOperation`.
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#8486 from cloud-fan/fix.
This PR takes over https://github.com/apache/spark/pull/8389.
This PR improves `checkAnswer` to print the partially analyzed plan in addition to the user friendly error message, in order to aid debugging failing tests.
In doing so, I ran into a conflict with the various ways that we bring a SQLContext into the tests. Depending on the trait we refer to the current context as `sqlContext`, `_sqlContext`, `ctx` or `hiveContext` with access modifiers `public`, `protected` and `private` depending on the defining class.
I propose we refactor as follows:
1. All tests should only refer to a `protected sqlContext` when testing general features, and `protected hiveContext` when it is a method that only exists on a `HiveContext`.
2. All tests should only import `testImplicits._` (i.e., don't import `TestHive.implicits._`)
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#8584 from cloud-fan/cleanupTests.
For example, we can write `SELECT MAX(value) FROM src GROUP BY key + 1 ORDER BY key + 1` in PostgreSQL, and we should support this in Spark SQL.
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#8548 from cloud-fan/support-order-by-non-attribute.
After this PR, In/InSet/ArrayContain will return null if value is null, instead of false. They also will return null even if there is a null in the set/array.
Author: Davies Liu <davies@databricks.com>
Closes#8492 from davies/fix_in.
This commit fixes an issue where the public SQL `Row` class did not override `hashCode`, causing it to violate the hashCode() + equals() contract. To fix this, I simply ported the `hashCode` implementation from the 1.4.x version of `Row`.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#8500 from JoshRosen/SPARK-10325 and squashes the following commits:
51ffea1 [Josh Rosen] Override hashCode() for public Row.
In BigDecimal or java.math.BigDecimal, the precision could be smaller than scale, for example, BigDecimal("0.001") has precision = 1 and scale = 3. But DecimalType require that the precision should be larger than scale, so we should use the maximum of precision and scale when inferring the schema from decimal literal.
Author: Davies Liu <davies@databricks.com>
Closes#8428 from davies/smaller_decimal.
Replace `JavaConversions` implicits with `JavaConverters`
Most occurrences I've seen so far are necessary conversions; a few have been avoidable. None are in critical code as far as I see, yet.
Author: Sean Owen <sowen@cloudera.com>
Closes#8033 from srowen/SPARK-9613.
We misunderstood the Julian days and nanoseconds of the day in parquet (as TimestampType) from Hive/Impala, they are overlapped, so can't be added together directly.
In order to avoid the confusing rounding when do the converting, we use `2440588` as the Julian Day of epoch of unix timestamp (which should be 2440587.5).
Author: Davies Liu <davies@databricks.com>
Author: Cheng Lian <lian@databricks.com>
Closes#8400 from davies/timestamp_parquet.
This patch adds an analyzer rule to ensure that set operations (union, intersect, and except) are only applied to tables with the same number of columns. Without this rule, there are scenarios where invalid queries can return incorrect results instead of failing with error messages; SPARK-9813 provides one example of this problem. In other cases, the invalid query can crash at runtime with extremely confusing exceptions.
I also performed a bit of cleanup to refactor some of those logical operators' code into a common `SetOperation` base class.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#7631 from JoshRosen/SPARK-9293.
Currently, we eagerly attempt to resolve functions, even before their children are resolved. However, this is not valid in cases where we need to know the types of the input arguments (i.e. when resolving Hive UDFs).
As a fix, this PR delays function resolution until the functions children are resolved. This change also necessitates a change to the way we resolve aggregate expressions that are not in aggregate operators (e.g., in `HAVING` or `ORDER BY` clauses). Specifically, we can't assume that these misplaced functions will be resolved, allowing us to differentiate aggregate functions from normal functions. To compensate for this change we now attempt to resolve these unresolved expressions in the context of the aggregate operator, before checking to see if any aggregate expressions are present.
Author: Michael Armbrust <michael@databricks.com>
Closes#8371 from marmbrus/hiveUDFResolution.
This adds a missing null check to the Decimal `toScala` converter in `CatalystTypeConverters`, fixing an NPE.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#8401 from JoshRosen/SPARK-10190.
Type coercion for IF should have children resolved first, or we could meet unresolved exception.
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#8331 from adrian-wang/spark10130.
This is based on #7779 , thanks to tarekauel . Fix the conflict and nullability.
Closes#7779 and #8274 .
Author: Tarek Auel <tarek.auel@googlemail.com>
Author: Davies Liu <davies@databricks.com>
Closes#8330 from davies/stringLocate.
https://issues.apache.org/jira/browse/SPARK-10092
This pr is a follow-up one for Multi-DB support. It has the following changes:
* `HiveContext.refreshTable` now accepts `dbName.tableName`.
* `HiveContext.analyze` now accepts `dbName.tableName`.
* `CreateTableUsing`, `CreateTableUsingAsSelect`, `CreateTempTableUsing`, `CreateTempTableUsingAsSelect`, `CreateMetastoreDataSource`, and `CreateMetastoreDataSourceAsSelect` all take `TableIdentifier` instead of the string representation of table name.
* When you call `saveAsTable` with a specified database, the data will be saved to the correct location.
* Explicitly do not allow users to create a temporary with a specified database name (users cannot do it before).
* When we save table to metastore, we also check if db name and table name can be accepted by hive (using `MetaStoreUtils.validateName`).
Author: Yin Huai <yhuai@databricks.com>
Closes#8324 from yhuai/saveAsTableDB.
A few minor changes:
1. Improved documentation
2. Rename apply(distinct....) to distinct.
3. Changed MutableAggregationBuffer from a trait to an abstract class.
4. Renamed returnDataType to dataType to be more consistent with other expressions.
And unrelated to UDAFs:
1. Renamed file names in expressions to use suffix "Expressions" to be more consistent.
2. Moved regexp related expressions out to its own file.
3. Renamed StringComparison => StringPredicate.
Author: Reynold Xin <rxin@databricks.com>
Closes#8321 from rxin/SPARK-9242.
create t1 (a decimal(7, 2), b long);
select case when 1=1 then a else 1.0 end from t1;
select case when 1=1 then a else b end from t1;
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#8270 from adrian-wang/casewhenfractional.
We should rounding the result of multiply/division of decimal to expected precision/scale, also check overflow.
Author: Davies Liu <davies@databricks.com>
Closes#8287 from davies/decimal_division.
This is kind of a weird case, but given a sufficiently complex query plan (in this case a TungstenProject with an Exchange underneath), we could have NPEs on the executors due to the time when we were calling transformAllExpressions
In general we should ensure that all transformations occur on the driver and not on the executors. Some reasons for avoid executor side transformations include:
* (this case) Some operator constructors require state such as access to the Spark/SQL conf so doing a makeCopy on the executor can fail.
* (unrelated reason for avoid executor transformations) ExprIds are calculated using an atomic integer, so you can violate their uniqueness constraint by constructing them anywhere other than the driver.
This subsumes #8285.
Author: Reynold Xin <rxin@databricks.com>
Author: Michael Armbrust <michael@databricks.com>
Closes#8295 from rxin/SPARK-10096.
In UnsafeRow, we use the private field of BigInteger for better performance, but it actually didn't contribute much (3% in one benchmark) to end-to-end runtime, and make it not portable (may fail on other JVM implementations).
So we should use the public API instead.
cc rxin
Author: Davies Liu <davies@databricks.com>
Closes#8286 from davies/portable_decimal.
The type for array of array in Java is slightly different than array of others.
cc cloud-fan
Author: Davies Liu <davies@databricks.com>
Closes#8250 from davies/array_binary.
https://issues.apache.org/jira/browse/SPARK-9592#8113 has the fundamental fix. But, if we want to minimize the number of changed lines, we can go with this one. Then, in 1.6, we merge #8113.
Author: Yin Huai <yhuai@databricks.com>
Closes#8172 from yhuai/lastFix and squashes the following commits:
b28c42a [Yin Huai] Regression test.
af87086 [Yin Huai] Fix last.
JIRA: https://issues.apache.org/jira/browse/SPARK-9526
This PR is a follow up of #7830, aiming at utilizing randomized tests to reveal more potential bugs in sql expression.
Author: Yijie Shen <henry.yijieshen@gmail.com>
Closes#7855 from yjshen/property_check.
We should skip unresolved `LogicalPlan`s for `PullOutNondeterministic`, as calling `output` on unresolved `LogicalPlan` will produce confusing error message.
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#8203 from cloud-fan/error-msg and squashes the following commits:
1c67ca7 [Wenchen Fan] move test
7593080 [Wenchen Fan] correct error message for aggregate
Also alias the ExtractValue instead of wrapping it with UnresolvedAlias when resolve attribute in LogicalPlan, as this alias will be trimmed if it's unnecessary.
Based on #7957 without the changes to mllib, but instead maintaining earlier behavior when using `withColumn` on expressions that already have metadata.
Author: Wenchen Fan <cloud0fan@outlook.com>
Author: Michael Armbrust <michael@databricks.com>
Closes#8215 from marmbrus/pr/7957.
As `InternalRow` does not extend `Row` now, I think we can remove it.
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#8170 from viirya/remove_canequal.
A fundamental limitation of the existing SQL tests is that *there is simply no way to create your own `SparkContext`*. This is a serious limitation because the user may wish to use a different master or config. As a case in point, `BroadcastJoinSuite` is entirely commented out because there is no way to make it pass with the existing infrastructure.
This patch removes the singletons `TestSQLContext` and `TestData`, and instead introduces a `SharedSQLContext` that starts a context per suite. Unfortunately the singletons were so ingrained in the SQL tests that this patch necessarily needed to touch *all* the SQL test files.
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Author: Andrew Or <andrew@databricks.com>
Closes#8111 from andrewor14/sql-tests-refactor.
PR #7967 enables us to save data source relations to metastore in Hive compatible format when possible. But it fails to persist Parquet relations with decimal column(s) to Hive metastore of versions lower than 1.2.0. This is because `ParquetHiveSerDe` in Hive versions prior to 1.2.0 doesn't support decimal. This PR checks for this case and falls back to Spark SQL specific metastore table format.
Author: Yin Huai <yhuai@databricks.com>
Author: Cheng Lian <lian@databricks.com>
Closes#8130 from liancheng/spark-9757/old-hive-parquet-decimal.
`RuleExecutor.timeMap` is currently a non-thread-safe mutable HashMap; this can lead to infinite loops if multiple threads are concurrently modifying the map. I believe that this is responsible for some hangs that I've observed in HiveQuerySuite.
This patch addresses this by using a Guava `AtomicLongMap`.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#8120 from JoshRosen/rule-executor-time-map-fix.
HashPartitioning compatibility is currently defined w.r.t the _set_ of expressions, but the ordering of those expressions matters when computing hash codes; this could lead to incorrect answers if we mistakenly avoided a shuffle based on the assumption that HashPartitionings with the same expressions in different orders will produce equivalent row hashcodes. The first commit adds a regression test which illustrates this problem.
The fix for this is simple: make `HashPartitioning.compatibleWith` and `HashPartitioning.guarantees` sensitive to the expression ordering (i.e. do not perform set comparison).
Author: Josh Rosen <joshrosen@databricks.com>
Closes#8074 from JoshRosen/hashpartitioning-compatiblewith-fixes and squashes the following commits:
b61412f [Josh Rosen] Demonstrate that I haven't cheated in my fix
0b4d7d9 [Josh Rosen] Update so that clusteringSet is only used in satisfies().
dc9c9d7 [Josh Rosen] Add failing regression test for SPARK-9785
PlatformDependent.UNSAFE is way too verbose.
Author: Reynold Xin <rxin@databricks.com>
Closes#8094 from rxin/SPARK-9815 and squashes the following commits:
229b603 [Reynold Xin] [SPARK-9815] Rename PlatformDependent.UNSAFE -> Platform.
This patch adds a new `SortMergeOuterJoin` operator that performs left and right outer joins using sort merge join. It also refactors `SortMergeJoin` in order to improve performance and code clarity.
Along the way, I also performed a couple pieces of minor cleanup and optimization:
- Rename the `HashJoin` physical planner rule to `EquiJoinSelection`, since it's also used for non-hash joins.
- Rewrite the comment at the top of `HashJoin` to better explain the precedence for choosing join operators.
- Update `JoinSuite` to use `SqlTestUtils.withConf` for changing SQLConf settings.
This patch incorporates several ideas from adrian-wang's patch, #5717.
Closes#5717.
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Author: Josh Rosen <joshrosen@databricks.com>
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#7904 from JoshRosen/outer-join-smj and squashes 1 commits.
This patch optimize two things:
1. passing MathContext to JavaBigDecimal.multiply/divide/reminder to do right rounding, because java.math.BigDecimal.apply(MathContext) is expensive
2. Cast integer/short/byte to decimal directly (without double)
This two optimizations could speed up the end-to-end time of a aggregation (SUM(short * decimal(5, 2)) 75% (from 19s -> 10.8s)
Author: Davies Liu <davies@databricks.com>
Closes#8052 from davies/optimize_decimal and squashes the following commits:
225efad [Davies Liu] improve decimal.times() and cast(int, decimalType)
Currently, generated UnsafeProjection can reach 64k byte code limit of Java. This patch will split the generated expressions into multiple functions, to avoid the limitation.
After this patch, we can work well with table that have up to 64k columns (hit max number of constants limit in Java), it should be enough in practice.
cc rxin
Author: Davies Liu <davies@databricks.com>
Closes#8044 from davies/wider_table and squashes the following commits:
9192e6c [Davies Liu] fix generated safe projection
d1ef81a [Davies Liu] fix failed tests
737b3d3 [Davies Liu] Merge branch 'master' of github.com:apache/spark into wider_table
ffcd132 [Davies Liu] address comments
1b95be4 [Davies Liu] put the generated class into sql package
77ed72d [Davies Liu] address comments
4518e17 [Davies Liu] Merge branch 'master' of github.com:apache/spark into wider_table
75ccd01 [Davies Liu] Merge branch 'master' of github.com:apache/spark into wider_table
495e932 [Davies Liu] support wider table with more than 1k columns for generated projections
This pull request refactors the `EnsureRequirements` planning rule in order to avoid the addition of certain unnecessary shuffles.
As an example of how unnecessary shuffles can occur, consider SortMergeJoin, which requires clustered distribution and sorted ordering of its children's input rows. Say that both of SMJ's children produce unsorted output but are both SinglePartition. In this case, we will need to inject sort operators but should not need to inject Exchanges. Unfortunately, it looks like the EnsureRequirements unnecessarily repartitions using a hash partitioning.
This patch solves this problem by refactoring `EnsureRequirements` to properly implement the `compatibleWith` checks that were broken in earlier implementations. See the significant inline comments for a better description of how this works. The majority of this PR is new comments and test cases, with few actual changes to the code.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#7988 from JoshRosen/exchange-fixes and squashes the following commits:
38006e7 [Josh Rosen] Rewrite EnsureRequirements _yet again_ to make things even simpler
0983f75 [Josh Rosen] More guarantees vs. compatibleWith cleanup; delete BroadcastPartitioning.
8784bd9 [Josh Rosen] Giant comment explaining compatibleWith vs. guarantees
1307c50 [Josh Rosen] Update conditions for requiring child compatibility.
18cddeb [Josh Rosen] Rename DummyPlan to DummySparkPlan.
2c7e126 [Josh Rosen] Merge remote-tracking branch 'origin/master' into exchange-fixes
fee65c4 [Josh Rosen] Further refinement to comments / reasoning
642b0bb [Josh Rosen] Further expand comment / reasoning
06aba0c [Josh Rosen] Add more comments
8dbc845 [Josh Rosen] Add even more tests.
4f08278 [Josh Rosen] Fix the test by adding the compatibility check to EnsureRequirements
a1c12b9 [Josh Rosen] Add failing test to demonstrate allCompatible bug
0725a34 [Josh Rosen] Small assertion cleanup.
5172ac5 [Josh Rosen] Add test for requiresChildrenToProduceSameNumberOfPartitions.
2e0f33a [Josh Rosen] Write a more generic test for EnsureRequirements.
752b8de [Josh Rosen] style fix
c628daf [Josh Rosen] Revert accidental ExchangeSuite change.
c9fb231 [Josh Rosen] Rewrite exchange to fix better handle this case.
adcc742 [Josh Rosen] Move test to PlannerSuite.
0675956 [Josh Rosen] Preserving ordering and partitioning in row format converters also does not help.
cc5669c [Josh Rosen] Adding outputPartitioning to Repartition does not fix the test.
2dfc648 [Josh Rosen] Add failing test illustrating bad exchange planning.
Author: Yijie Shen <henry.yijieshen@gmail.com>
Closes#8057 from yjshen/explode_star and squashes the following commits:
eae181d [Yijie Shen] change explaination message
54c9d11 [Yijie Shen] meaning message for * in explode
In https://github.com/apache/spark/pull/7752 we added `FromUnsafe` to convert nexted unsafe data like array/map/struct to safe versions. It's a quick solution and we already have `GenerateSafe` to do the conversion which is codegened. So we should remove `FromUnsafe` and implement its codegen version in `GenerateSafe`.
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#8029 from cloud-fan/from-unsafe and squashes the following commits:
ed40d8f [Wenchen Fan] add the copy back
a93fd4b [Wenchen Fan] cogengen FromUnsafe
All data sources show up as "PhysicalRDD" in physical plan explain. It'd be better if we can show the name of the data source.
Without this patch:
```
== Physical Plan ==
NewAggregate with UnsafeHybridAggregationIterator ArrayBuffer(date#0, cat#1) ArrayBuffer((sum(CAST((CAST(count#2, IntegerType) + 1), LongType))2,mode=Final,isDistinct=false))
Exchange hashpartitioning(date#0,cat#1)
NewAggregate with UnsafeHybridAggregationIterator ArrayBuffer(date#0, cat#1) ArrayBuffer((sum(CAST((CAST(count#2, IntegerType) + 1), LongType))2,mode=Partial,isDistinct=false))
PhysicalRDD [date#0,cat#1,count#2], MapPartitionsRDD[3] at
```
With this patch:
```
== Physical Plan ==
TungstenAggregate(key=[date#0,cat#1], value=[(sum(CAST((CAST(count#2, IntegerType) + 1), LongType)),mode=Final,isDistinct=false)]
Exchange hashpartitioning(date#0,cat#1)
TungstenAggregate(key=[date#0,cat#1], value=[(sum(CAST((CAST(count#2, IntegerType) + 1), LongType)),mode=Partial,isDistinct=false)]
ConvertToUnsafe
Scan ParquetRelation[file:/scratch/rxin/spark/sales4][date#0,cat#1,count#2]
```
Author: Reynold Xin <rxin@databricks.com>
Closes#8024 from rxin/SPARK-9733 and squashes the following commits:
811b90e [Reynold Xin] Fixed Python test case.
52cab77 [Reynold Xin] Cast.
eea9ccc [Reynold Xin] Fix test case.
fcecb22 [Reynold Xin] [SPARK-9733][SQL] Improve explain message for data source scan node.
JoinedRow.anyNull currently loops through every field to check for null, which is inefficient if the underlying rows are UnsafeRows. It should just delegate to the underlying implementation.
Author: Reynold Xin <rxin@databricks.com>
Closes#8027 from rxin/SPARK-9736 and squashes the following commits:
03a2e92 [Reynold Xin] Include all files.
90f1add [Reynold Xin] [SPARK-9736][SQL] JoinedRow.anyNull should delegate to the underlying rows.
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#8025 from cloud-fan/analysis and squashes the following commits:
51461b1 [Wenchen Fan] move test file to test folder
ec88ace [Wenchen Fan] Improve Analysis Unit test framework
When we convert unsafe row to safe row, we will do copy if the column is struct or string type. However, the string inside unsafe array/map are not copied, which may cause problems.
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#7990 from cloud-fan/copy and squashes the following commits:
c13d1e3 [Wenchen Fan] change test name
fe36294 [Wenchen Fan] we should deep copy UTF8String when convert unsafe row to safe row
Make sure that `$"column"` is consistent with other methods with respect to backticks. Adds a bunch of tests for various ways of constructing columns.
Author: Michael Armbrust <michael@databricks.com>
Closes#7969 from marmbrus/namesWithDots and squashes the following commits:
53ef3d7 [Michael Armbrust] [SPARK-9650][SQL] Fix quoting behavior on interpolated column names
2bf7a92 [Michael Armbrust] WIP
This is the followup of https://github.com/apache/spark/pull/7813. It renames `HybridUnsafeAggregationIterator` to `TungstenAggregationIterator` and makes it only work with `UnsafeRow`. Also, I add a `TungstenAggregate` that uses `TungstenAggregationIterator` and make `SortBasedAggregate` (renamed from `SortBasedAggregate`) only works with `SafeRow`.
Author: Yin Huai <yhuai@databricks.com>
Closes#7954 from yhuai/agg-followUp and squashes the following commits:
4d2f4fc [Yin Huai] Add comments and free map.
0d7ddb9 [Yin Huai] Add TungstenAggregationQueryWithControlledFallbackSuite to test fall back process.
91d69c2 [Yin Huai] Rename UnsafeHybridAggregationIterator to TungstenAggregateIteraotr and make it only work with UnsafeRow.
This re-applies #7955, which was reverted due to a race condition to fix build breaking.
Author: Wenchen Fan <cloud0fan@outlook.com>
Author: Reynold Xin <rxin@databricks.com>
Closes#8002 from rxin/InternalRow-toSeq and squashes the following commits:
332416a [Reynold Xin] Merge pull request #7955 from cloud-fan/toSeq
21665e2 [Wenchen Fan] fix hive again...
4addf29 [Wenchen Fan] fix hive
bc16c59 [Wenchen Fan] minor fix
33d802c [Wenchen Fan] pass data type info to InternalRow.toSeq
3dd033e [Wenchen Fan] move the default special getters implementation from InternalRow to BaseGenericInternalRow
seems https://github.com/apache/spark/pull/7955 breaks the build.
Author: Yin Huai <yhuai@databricks.com>
Closes#8001 from yhuai/SPARK-9632-fixBuild and squashes the following commits:
6c257dd [Yin Huai] Fix build.
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#7955 from cloud-fan/toSeq and squashes the following commits:
21665e2 [Wenchen Fan] fix hive again...
4addf29 [Wenchen Fan] fix hive
bc16c59 [Wenchen Fan] minor fix
33d802c [Wenchen Fan] pass data type info to InternalRow.toSeq
3dd033e [Wenchen Fan] move the default special getters implementation from InternalRow to BaseGenericInternalRow
In order to support update a varlength (actually fixed length) object, the space should be preserved even it's null. And, we can't call setNullAt(i) for it anymore, we because setNullAt(i) will remove the offset of the preserved space, should call setDecimal(i, null, precision) instead.
After this, we can do hash based aggregation on DecimalType with precision > 18. In a tests, this could decrease the end-to-end run time of aggregation query from 37 seconds (sort based) to 24 seconds (hash based).
cc rxin
Author: Davies Liu <davies@databricks.com>
Closes#7978 from davies/update_decimal and squashes the following commits:
bed8100 [Davies Liu] isSettable -> isMutable
923c9eb [Davies Liu] address comments and fix bug
385891d [Davies Liu] Merge branch 'master' of github.com:apache/spark into update_decimal
36a1872 [Davies Liu] fix tests
cd6c524 [Davies Liu] support set decimal with precision > 18
Author: zhichao.li <zhichao.li@intel.com>
Closes#7709 from zhichao-li/translate and squashes the following commits:
9418088 [zhichao.li] refine checking condition
f2ab77a [zhichao.li] clone string
9d88f2d [zhichao.li] fix indent
6aa2962 [zhichao.li] style
e575ead [zhichao.li] add python api
9d4bab0 [zhichao.li] add special case for fodable and refactor unittest
eda7ad6 [zhichao.li] update to use TernaryExpression
cdfd4be [zhichao.li] add function translate
This patches renames `RowOrdering` to `InterpretedOrdering` and updates SortMergeJoin to use the `SparkPlan` methods for constructing its ordering so that it may benefit from codegen.
This is an updated version of #7408.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#7973 from JoshRosen/SPARK-9054 and squashes the following commits:
e610655 [Josh Rosen] Add comment RE: Ascending ordering
34b8e0c [Josh Rosen] Import ordering
be19a0f [Josh Rosen] [SPARK-9054] [SQL] Rename RowOrdering to InterpretedOrdering; use newOrdering in more places.
This continues tarekauel's work in #7778.
Author: Liang-Chi Hsieh <viirya@appier.com>
Author: Tarek Auel <tarek.auel@googlemail.com>
Closes#7893 from viirya/codegen_in and squashes the following commits:
81ff97b [Liang-Chi Hsieh] For comments.
47761c6 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into codegen_in
cf4bf41 [Liang-Chi Hsieh] For comments.
f532b3c [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into codegen_in
446bbcd [Liang-Chi Hsieh] Fix bug.
b3d0ab4 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into codegen_in
4610eff [Liang-Chi Hsieh] Relax the types of references and update optimizer test.
224f18e [Liang-Chi Hsieh] Beef up the test cases for In and InSet to include all primitive data types.
86dc8aa [Liang-Chi Hsieh] Only convert In to InSet when the number of items in set is more than the threshold.
b7ded7e [Tarek Auel] [SPARK-9403][SQL] codeGen in / inSet
This is a follow-up of https://github.com/apache/spark/pull/7920 to fix comments.
Author: Yin Huai <yhuai@databricks.com>
Closes#7964 from yhuai/SPARK-9141-follow-up and squashes the following commits:
4d0ee80 [Yin Huai] Fix comments.
Currently we collapse successive projections that are added by `withColumn`. However, this optimization violates the constraint that adding nodes to a plan will never change its analyzed form and thus breaks caching. Instead of doing early optimization, in this PR I just fix some low-hanging slowness in the analyzer. In particular, I add a mechanism for skipping already analyzed subplans, `resolveOperators` and `resolveExpression`. Since trees are generally immutable after construction, it's safe to annotate a plan as already analyzed as any transformation will create a new tree with this bit no longer set.
Together these result in a faster analyzer than before, even with added timing instrumentation.
```
Original Code
[info] 3430ms
[info] 2205ms
[info] 1973ms
[info] 1982ms
[info] 1916ms
Without Project Collapsing in DataFrame
[info] 44610ms
[info] 45977ms
[info] 46423ms
[info] 46306ms
[info] 54723ms
With analyzer optimizations
[info] 6394ms
[info] 4630ms
[info] 4388ms
[info] 4093ms
[info] 4113ms
With resolveOperators
[info] 2495ms
[info] 1380ms
[info] 1685ms
[info] 1414ms
[info] 1240ms
```
Author: Michael Armbrust <michael@databricks.com>
Closes#7920 from marmbrus/withColumnCache and squashes the following commits:
2145031 [Michael Armbrust] fix hive udfs tests
5a5a525 [Michael Armbrust] remove wrong comment
7a507d5 [Michael Armbrust] style
b59d710 [Michael Armbrust] revert small change
1fa5949 [Michael Armbrust] move logic into LogicalPlan, add tests
0e2cb43 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into withColumnCache
c926e24 [Michael Armbrust] naming
e593a2d [Michael Armbrust] style
f5a929e [Michael Armbrust] [SPARK-9141][SQL] Remove project collapsing from DataFrame API
38b1c83 [Michael Armbrust] WIP
JIRA: https://issues.apache.org/jira/browse/SPARK-9628
Author: Yijie Shen <henry.yijieshen@gmail.com>
Closes#7953 from yjshen/datetime_alias and squashes the following commits:
3cac3cc [Yijie Shen] rename int to SQLDate, long to SQLTimestamp for better readability
The current implementation of UnsafeExternalSort uses NoOpPrefixComparator for binary-typed data.
So, we need to add BinaryPrefixComparator in PrefixComparators.
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Closes#7676 from maropu/BinaryTypePrefixComparator and squashes the following commits:
fe6f31b [Takeshi YAMAMURO] Apply comments
d943c04 [Takeshi YAMAMURO] Add a codegen'd entry for BinaryType in SortPrefix
ecf3ac5 [Takeshi YAMAMURO] Support BinaryType in PrefixComparator
Let Decimal carry the correct precision and scale with DecimalType.
cc rxin yhuai
Author: Davies Liu <davies@databricks.com>
Closes#7925 from davies/decimal_scale and squashes the following commits:
e19701a [Davies Liu] some tweaks
57d78d2 [Davies Liu] fix tests
5d5bc69 [Davies Liu] match precision and scale with DecimalType
This PR is based on #7580 , thanks to EntilZha
PR for work on https://issues.apache.org/jira/browse/SPARK-8231
Currently, I have an initial implementation for contains. Based on discussion on JIRA, it should behave same as Hive: https://github.com/apache/hive/blob/master/ql/src/java/org/apache/hadoop/hive/ql/udf/generic/GenericUDFArrayContains.java#L102-L128
Main points are:
1. If the array is empty, null, or the value is null, return false
2. If there is a type mismatch, throw error
3. If comparison is not supported, throw error
Closes#7580
Author: Pedro Rodriguez <prodriguez@trulia.com>
Author: Pedro Rodriguez <ski.rodriguez@gmail.com>
Author: Davies Liu <davies@databricks.com>
Closes#7949 from davies/array_contains and squashes the following commits:
d3c08bc [Davies Liu] use foreach() to avoid copy
bc3d1fe [Davies Liu] fix array_contains
719e37d [Davies Liu] Merge branch 'master' of github.com:apache/spark into array_contains
e352cf9 [Pedro Rodriguez] fixed diff from master
4d5b0ff [Pedro Rodriguez] added docs and another type check
ffc0591 [Pedro Rodriguez] fixed unit test
7a22deb [Pedro Rodriguez] Changed test to use strings instead of long/ints which are different between python 2 an 3
b5ffae8 [Pedro Rodriguez] fixed pyspark test
4e7dce3 [Pedro Rodriguez] added more docs
3082399 [Pedro Rodriguez] fixed unit test
46f9789 [Pedro Rodriguez] reverted change
d3ca013 [Pedro Rodriguez] Fixed type checking to match hive behavior, then added tests to insure this
8528027 [Pedro Rodriguez] added more tests
686e029 [Pedro Rodriguez] fix scala style
d262e9d [Pedro Rodriguez] reworked type checking code and added more tests
2517a58 [Pedro Rodriguez] removed unused import
28b4f71 [Pedro Rodriguez] fixed bug with type conversions and re-added tests
12f8795 [Pedro Rodriguez] fix scala style checks
e8a20a9 [Pedro Rodriguez] added python df (broken atm)
65b562c [Pedro Rodriguez] made array_contains nullable false
33b45aa [Pedro Rodriguez] reordered test
9623c64 [Pedro Rodriguez] fixed test
4b4425b [Pedro Rodriguez] changed Arrays in tests to Seqs
72cb4b1 [Pedro Rodriguez] added checkInputTypes and docs
69c46fb [Pedro Rodriguez] added tests and codegen
9e0bfc4 [Pedro Rodriguez] initial attempt at implementation
JIRA: https://issues.apache.org/jira/browse/SPARK-9432
Author: Yijie Shen <henry.yijieshen@gmail.com>
Closes#7933 from yjshen/numeric_ranges and squashes the following commits:
e719f78 [Yijie Shen] proper integral range check
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#7932 from cloud-fan/generic-getter and squashes the following commits:
c60de4c [Wenchen Fan] do not expose generic getter in internal row
This patch extends UnsafeExternalSorter to support records larger than the page size. The basic strategy is the same as in #7762: store large records in their own overflow pages.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#7891 from JoshRosen/large-records-in-sql-sorter and squashes the following commits:
967580b [Josh Rosen] Merge remote-tracking branch 'origin/master' into large-records-in-sql-sorter
948c344 [Josh Rosen] Add large records tests for KV sorter.
3c17288 [Josh Rosen] Combine memory and disk cleanup into general cleanupResources() method
380f217 [Josh Rosen] Merge remote-tracking branch 'origin/master' into large-records-in-sql-sorter
27eafa0 [Josh Rosen] Fix page size in PackedRecordPointerSuite
a49baef [Josh Rosen] Address initial round of review comments
3edb931 [Josh Rosen] Remove accidentally-committed debug statements.
2b164e2 [Josh Rosen] Support large records in UnsafeExternalSorter.
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#7890 from cloud-fan/minor and squashes the following commits:
c3b1be3 [Wenchen Fan] fix style
b0cbe2e [Wenchen Fan] remove the createCode and createStructCode, and replace the usage of them by createStructCode
The analysis rule has a bug and we ended up making the sorter still capable of doing evaluation, so lets revert this for now.
Author: Michael Armbrust <michael@databricks.com>
Closes#7906 from marmbrus/revertSortProjection and squashes the following commits:
2da6972 [Michael Armbrust] unrevert unrelated changes
4f2b00c [Michael Armbrust] Revert "[SPARK-9251][SQL] do not order by expressions which still need evaluation"
This is based on #7485 , thanks to NathanHowell
Tests were copied from Hive, but do not seem to be super comprehensive. I've generally replicated Hive's unusual behavior rather than following a JSONPath reference, except for one case (as noted in the comments). I don't know if there is a way of fully replicating Hive's behavior without a slower TreeNode implementation, so I've erred on the side of performance instead.
Author: Davies Liu <davies@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Author: Nathan Howell <nhowell@godaddy.com>
Closes#7901 from davies/get_json_object and squashes the following commits:
3ace9b9 [Davies Liu] Merge branch 'get_json_object' of github.com:davies/spark into get_json_object
98766fc [Davies Liu] Merge branch 'master' of github.com:apache/spark into get_json_object
a7dc6d0 [Davies Liu] Update JsonExpressionsSuite.scala
c818519 [Yin Huai] new results.
18ce26b [Davies Liu] fix tests
6ac29fb [Yin Huai] Golden files.
25eebef [Davies Liu] use HiveQuerySuite
e0ac6ec [Yin Huai] Golden answer files.
940c060 [Davies Liu] tweat code style
44084c5 [Davies Liu] Merge branch 'master' of github.com:apache/spark into get_json_object
9192d09 [Nathan Howell] Match Hive’s behavior for unwrapping arrays of one element
8dab647 [Nathan Howell] [SPARK-8246] [SQL] Implement get_json_object
This PR is based on #7186 (just fix the conflict), thanks to tarekauel .
find_in_set(string str, string strList): int
Returns the first occurance of str in strList where strList is a comma-delimited string. Returns null if either argument is null. Returns 0 if the first argument contains any commas. For example, find_in_set('ab', 'abc,b,ab,c,def') returns 3.
Only add this to SQL, not DataFrame.
Closes#7186
Author: Tarek Auel <tarek.auel@googlemail.com>
Author: Davies Liu <davies@databricks.com>
Closes#7900 from davies/find_in_set and squashes the following commits:
4334209 [Davies Liu] Merge branch 'master' of github.com:apache/spark into find_in_set
8f00572 [Davies Liu] Merge branch 'master' of github.com:apache/spark into find_in_set
243ede4 [Tarek Auel] [SPARK-8244][SQL] hive compatibility
1aaf64e [Tarek Auel] [SPARK-8244][SQL] unit test fix
e4093a4 [Tarek Auel] [SPARK-8244][SQL] final modifier for COMMA_UTF8
0d05df5 [Tarek Auel] Merge branch 'master' into SPARK-8244
208d710 [Tarek Auel] [SPARK-8244] address comments & bug fix
71b2e69 [Tarek Auel] [SPARK-8244] find_in_set
66c7fda [Tarek Auel] Merge branch 'master' into SPARK-8244
61b8ca2 [Tarek Auel] [SPARK-8224] removed loop and split; use unsafe String comparison
4f75a65 [Tarek Auel] Merge branch 'master' into SPARK-8244
e3b20c8 [Tarek Auel] [SPARK-8244] added type check
1c2bbb7 [Tarek Auel] [SPARK-8244] findInSet
Cherry picked the parts of the initial SPARK-8064 WiP branch needed to get sql/hive to compile against hive 1.2.1. That's the ASF release packaged under org.apache.hive, not any fork.
Tests not run yet: that's what the machines are for
Author: Steve Loughran <stevel@hortonworks.com>
Author: Cheng Lian <lian@databricks.com>
Author: Michael Armbrust <michael@databricks.com>
Author: Patrick Wendell <patrick@databricks.com>
Closes#7191 from steveloughran/stevel/feature/SPARK-8064-hive-1.2-002 and squashes the following commits:
7556d85 [Cheng Lian] Updates .q files and corresponding golden files
ef4af62 [Steve Loughran] Merge commit '6a92bb09f46a04d6cd8c41bdba3ecb727ebb9030' into stevel/feature/SPARK-8064-hive-1.2-002
6a92bb0 [Cheng Lian] Overrides HiveConf time vars
dcbb391 [Cheng Lian] Adds com.twitter:parquet-hadoop-bundle:1.6.0 for Hive Parquet SerDe
0bbe475 [Steve Loughran] SPARK-8064 scalastyle rejects the standard Hadoop ASF license header...
fdf759b [Steve Loughran] SPARK-8064 classpath dependency suite to be in sync with shading in final (?) hive-exec spark
7a6c727 [Steve Loughran] SPARK-8064 switch to second staging repo of the spark-hive artifacts. This one has the protobuf-shaded hive-exec jar
376c003 [Steve Loughran] SPARK-8064 purge duplicate protobuf declaration
2c74697 [Steve Loughran] SPARK-8064 switch to the protobuf shaded hive-exec jar with tests to chase it down
cc44020 [Steve Loughran] SPARK-8064 remove hadoop.version from runtest.py, as profile will fix that automatically.
6901fa9 [Steve Loughran] SPARK-8064 explicit protobuf import
da310dc [Michael Armbrust] Fixes for Hive tests.
a775a75 [Steve Loughran] SPARK-8064 cherry-pick-incomplete
7404f34 [Patrick Wendell] Add spark-hive staging repo
832c164 [Steve Loughran] SPARK-8064 try to supress compiler warnings on Complex.java pasted-thrift-code
312c0d4 [Steve Loughran] SPARK-8064 maven/ivy dependency purge; calcite declaration needed
fa5ae7b [Steve Loughran] HIVE-8064 fix up hive-thriftserver dependencies and cut back on evicted references in the hive- packages; this keeps mvn and ivy resolution compatible, as the reconciliation policy is "by hand"
c188048 [Steve Loughran] SPARK-8064 manage the Hive depencencies to that -things that aren't needed are excluded -sql/hive built with ivy is in sync with the maven reconciliation policy, rather than latest-first
4c8be8d [Cheng Lian] WIP: Partial fix for Thrift server and CLI tests
314eb3c [Steve Loughran] SPARK-8064 deprecation warning noise in one of the tests
17b0341 [Steve Loughran] SPARK-8064 IDE-hinted cleanups of Complex.java to reduce compiler warnings. It's all autogenerated code, so still ugly.
d029b92 [Steve Loughran] SPARK-8064 rely on unescaping to have already taken place, so go straight to map of serde options
23eca7e [Steve Loughran] HIVE-8064 handle raw and escaped property tokens
54d9b06 [Steve Loughran] SPARK-8064 fix compilation regression surfacing from rebase
0b12d5f [Steve Loughran] HIVE-8064 use subset of hive complex type whose types deserialize
fce73b6 [Steve Loughran] SPARK-8064 poms rely implicitly on the version of kryo chill provides
fd3aa5d [Steve Loughran] SPARK-8064 version of hive to d/l from ivy is 1.2.1
dc73ece [Steve Loughran] SPARK-8064 revert to master's determinstic pushdown strategy
d3c1e4a [Steve Loughran] SPARK-8064 purge UnionType
051cc21 [Steve Loughran] SPARK-8064 switch to an unshaded version of hive-exec-core, which must have been built with Kryo 2.21. This currently looks for a (locally built) version 1.2.1.spark
6684c60 [Steve Loughran] SPARK-8064 ignore RTE raised in blocking process.exitValue() call
e6121e5 [Steve Loughran] SPARK-8064 address review comments
aa43dc6 [Steve Loughran] SPARK-8064 more robust teardown on JavaMetastoreDatasourcesSuite
f2bff01 [Steve Loughran] SPARK-8064 better takeup of asynchronously caught error text
8b1ef38 [Steve Loughran] SPARK-8064: on failures executing spark-submit in HiveSparkSubmitSuite, print command line and all logged output.
5a9ce6b [Steve Loughran] SPARK-8064 add explicit reason for kv split failure, rather than array OOB. *does not address the issue*
642b63a [Steve Loughran] SPARK-8064 reinstate something cut briefly during rebasing
97194dc [Steve Loughran] SPARK-8064 add extra logging to the YarnClusterSuite classpath test. There should be no reason why this is failing on jenkins, but as it is (and presumably its CP-related), improve the logging including any exception raised.
335357f [Steve Loughran] SPARK-8064 fail fast on thrive process spawning tests on exit codes and/or error string patterns seen in log.
3ed872f [Steve Loughran] SPARK-8064 rename field double to dbl
bca55e5 [Steve Loughran] SPARK-8064 missed one of the `date` escapes
41d6479 [Steve Loughran] SPARK-8064 wrap tests with withTable() calls to avoid table-exists exceptions
2bc29a4 [Steve Loughran] SPARK-8064 ParquetSuites to escape `date` field name
1ab9bc4 [Steve Loughran] SPARK-8064 TestHive to use sered2.thrift.test.Complex
bf3a249 [Steve Loughran] SPARK-8064: more resubmit than fix; tighten startup timeout to 60s. Still no obvious reason why jersey server code in spark-assembly isn't being picked up -it hasn't been shaded
c829b8f [Steve Loughran] SPARK-8064: reinstate yarn-rm-server dependencies to hive-exec to ensure that jersey server is on classpath on hadoop versions < 2.6
0b0f738 [Steve Loughran] SPARK-8064: thrift server startup to fail fast on any exception in the main thread
13abaf1 [Steve Loughran] SPARK-8064 Hive compatibilty tests sin sync with explain/show output from Hive 1.2.1
d14d5ea [Steve Loughran] SPARK-8064: DATE is now a predicate; you can't use it as a field in select ops
26eef1c [Steve Loughran] SPARK-8064: HIVE-9039 renamed TOK_UNION => TOK_UNIONALL while adding TOK_UNIONDISTINCT
3d64523 [Steve Loughran] SPARK-8064 improve diagns on uknown token; fix scalastyle failure
d0360f6 [Steve Loughran] SPARK-8064: delicate merge in of the branch vanzin/hive-1.1
1126e5a [Steve Loughran] SPARK-8064: name of unrecognized file format wasn't appearing in error text
8cb09c4 [Steve Loughran] SPARK-8064: test resilience/assertion improvements. Independent of the rest of the work; can be backported to earlier versions
dec12cb [Steve Loughran] SPARK-8064: when a CLI suite test fails include the full output text in the raised exception; this ensures that the stdout/stderr is included in jenkins reports, so it becomes possible to diagnose the cause.
463a670 [Steve Loughran] SPARK-8064 run-tests.py adds a hadoop-2.6 profile, and changes info messages to say "w/Hive 1.2.1" in console output
2531099 [Steve Loughran] SPARK-8064 successful attempt to get rid of pentaho as a transitive dependency of hive-exec
1d59100 [Steve Loughran] SPARK-8064 (unsuccessful) attempt to get rid of pentaho as a transitive dependency of hive-exec
75733fc [Steve Loughran] SPARK-8064 change thrift binary startup message to "Starting ThriftBinaryCLIService on port"
3ebc279 [Steve Loughran] SPARK-8064 move strings used to check for http/bin thrift services up into constants
c80979d [Steve Loughran] SPARK-8064: SparkSQLCLIDriver drops remote mode support. CLISuite Tests pass instead of timing out: undetected regression?
27e8370 [Steve Loughran] SPARK-8064 fix some style & IDE warnings
00e50d6 [Steve Loughran] SPARK-8064 stop excluding hive shims from dependency (commented out , for now)
cb4f142 [Steve Loughran] SPARK-8054 cut pentaho dependency from calcite
f7aa9cb [Steve Loughran] SPARK-8064 everything compiles with some commenting and moving of classes into a hive package
6c310b4 [Steve Loughran] SPARK-8064 subclass Hive ServerOptionsProcessor to make it public again
f61a675 [Steve Loughran] SPARK-8064 thrift server switched to Hive 1.2.1, though it doesn't compile everywhere
4890b9d [Steve Loughran] SPARK-8064, build against Hive 1.2.1
Author: Reynold Xin <rxin@databricks.com>
Closes#7897 from rxin/calculateBitSetWidthInBytes and squashes the following commits:
2e73b3a [Reynold Xin] [SQL][minor] Simplify UnsafeRow.calculateBitSetWidthInBytes.
The issue was that the tokenizer was parsing "1one" into the numeric 1 using the code on line 110. I added another case to accept strings that start with a number and then have a letter somewhere else in it as well.
Author: Joseph Batchik <joseph.batchik@cloudera.com>
Closes#7844 from JDrit/parse_error and squashes the following commits:
b8ca12f [Joseph Batchik] fixed parsing issue by adding another case
Currently, when copy the bitsets, we didn't consider that the row1 may not sit in the beginning of byte array.
cc rxin
Author: Davies Liu <davies@databricks.com>
Closes#7892 from davies/clean_join and squashes the following commits:
14cce9e [Davies Liu] cleanup generated UnsafeRowJoiner and fix bug
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#7885 from cloud-fan/cheap-copy and squashes the following commits:
0900ca1 [Wenchen Fan] replace == with ===
73f4ada [Wenchen Fan] add tests
07b865a [Wenchen Fan] add a cheap version of copy
This PR adds a base aggregation iterator `AggregationIterator`, which is used to create `SortBasedAggregationIterator` (for sort-based aggregation) and `UnsafeHybridAggregationIterator` (first it tries hash-based aggregation and falls back to the sort-based aggregation (using external sorter) if we cannot allocate memory for the map). With these two iterators, we will not need existing iterators and I am removing those. Also, we can use a single physical `Aggregate` operator and it internally determines what iterators to used.
https://issues.apache.org/jira/browse/SPARK-9240
Author: Yin Huai <yhuai@databricks.com>
Closes#7813 from yhuai/AggregateOperator and squashes the following commits:
e317e2b [Yin Huai] Remove unnecessary change.
74d93c5 [Yin Huai] Merge remote-tracking branch 'upstream/master' into AggregateOperator
ba6afbc [Yin Huai] Add a little bit more comments.
c9cf3b6 [Yin Huai] update
0f1b06f [Yin Huai] Remove unnecessary code.
21fd15f [Yin Huai] Remove unnecessary change.
964f88b [Yin Huai] Implement fallback strategy.
b1ea5cf [Yin Huai] wip
7fcbd87 [Yin Huai] Add a flag to control what iterator to use.
533d5b2 [Yin Huai] Prepare for fallback!
33b7022 [Yin Huai] wip
bd9282b [Yin Huai] UDAFs now supports UnsafeRow.
f52ee53 [Yin Huai] wip
3171f44 [Yin Huai] wip
d2c45a0 [Yin Huai] wip
f60cc83 [Yin Huai] Also check input schema.
af32210 [Yin Huai] Check iter.hasNext before we create an iterator because the constructor of the iterato will read at least one row from a non-empty input iter.
299008c [Yin Huai] First round cleanup.
3915bac [Yin Huai] Create a base iterator class for aggregation iterators and add the initial version of the hybrid iterator.
JIRA: https://issues.apache.org/jira/browse/SPARK-9549
This PR fix the following bugs:
1. `UnaryMinus`'s codegen version would fail to compile when the input is `Long.MinValue`
2. `BinaryComparison` would fail to compile in codegen mode when comparing Boolean types.
3. `AddMonth` would fail if passed a huge negative month, which would lead accessing negative index of `monthDays` array.
4. `Nanvl` with different type operands.
Author: Yijie Shen <henry.yijieshen@gmail.com>
Closes#7882 from yjshen/minor_bug_fix and squashes the following commits:
41bbd2c [Yijie Shen] fix bug in Nanvl type coercion
3dee204 [Yijie Shen] address comments
4fa5de0 [Yijie Shen] fix bugs in expressions
This PR adds a UnsafeArrayData, current we encode it in this way:
first 4 bytes is the # elements
then each 4 byte is the start offset of the element, unless it is negative, in which case the element is null.
followed by the elements themselves
an example: [10, 11, 12, 13, null, 14] will be encoded as:
5, 28, 32, 36, 40, -44, 44, 10, 11, 12, 13, 14
Note that, when we read a UnsafeArrayData from bytes, we can read the first 4 bytes as numElements and take the rest(first 4 bytes skipped) as value region.
unsafe map data just use 2 unsafe array data, first 4 bytes is # of elements, second 4 bytes is numBytes of key array, the follows key array data and value array data.
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#7752 from cloud-fan/unsafe-array and squashes the following commits:
3269bd7 [Wenchen Fan] fix a bug
6445289 [Wenchen Fan] add unit tests
49adf26 [Wenchen Fan] add unsafe map
20d1039 [Wenchen Fan] add comments and unsafe converter
821b8db [Wenchen Fan] add unsafe array
This PR adds an optimization rule, `FilterNullsInJoinKey`, to add `Filter` before join operators to filter out rows having null values for join keys.
This optimization is guarded by a new SQL conf, `spark.sql.advancedOptimization`.
The code in this PR was authored by yhuai; I'm opening this PR to factor out this change from #7685, a larger pull request which contains two other optimizations.
Author: Yin Huai <yhuai@databricks.com>
Author: Josh Rosen <joshrosen@databricks.com>
Closes#7768 from JoshRosen/filter-nulls-in-join-key and squashes the following commits:
c02fc3f [Yin Huai] Address Josh's comments.
0a8e096 [Yin Huai] Update comments.
ea7d5a6 [Yin Huai] Make sure we do not keep adding filters.
be88760 [Yin Huai] Make it clear that FilterNullsInJoinKeySuite.scala is used to test FilterNullsInJoinKey.
8bb39ad [Yin Huai] Fix non-deterministic tests.
303236b [Josh Rosen] Revert changes that are unrelated to null join key filtering
40eeece [Josh Rosen] Merge remote-tracking branch 'origin/master' into filter-nulls-in-join-key
c57a954 [Yin Huai] Bug fix.
d3d2e64 [Yin Huai] First round of cleanup.
f9516b0 [Yin Huai] Style
c6667e7 [Yin Huai] Add PartitioningCollection.
e616d3b [Yin Huai] wip
7c2d2d8 [Yin Huai] Bug fix and refactoring.
69bb072 [Yin Huai] Introduce NullSafeHashPartitioning and NullUnsafePartitioning.
d5b84c3 [Yin Huai] Do not add unnessary filters.
2201129 [Yin Huai] Filter out rows that will not be joined in equal joins early.
This PR adds `PartitioningCollection`, which is used to represent the `outputPartitioning` for SparkPlans with multiple children (e.g. `ShuffledHashJoin`). So, a `SparkPlan` can have multiple descriptions of its partitioning schemes. Taking `ShuffledHashJoin` as an example, it has two descriptions of its partitioning schemes, i.e. `left.outputPartitioning` and `right.outputPartitioning`. So when we have a query like `select * from t1 join t2 on (t1.x = t2.x) join t3 on (t2.x = t3.x)` will only have three Exchange operators (when shuffled joins are needed) instead of four.
The code in this PR was authored by yhuai; I'm opening this PR to factor out this change from #7685, a larger pull request which contains two other optimizations.
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Author: Yin Huai <yhuai@databricks.com>
Author: Josh Rosen <joshrosen@databricks.com>
Closes#7773 from JoshRosen/multi-way-join-planning-improvements and squashes the following commits:
5c45924 [Josh Rosen] Merge remote-tracking branch 'origin/master' into multi-way-join-planning-improvements
cd8269b [Josh Rosen] Refactor test to use SQLTestUtils
2963857 [Yin Huai] Revert unnecessary SqlConf change.
73913f7 [Yin Huai] Add comments and test. Also, revert the change in ShuffledHashOuterJoin for now.
4a99204 [Josh Rosen] Delete unrelated expression change
884ab95 [Josh Rosen] Carve out only SPARK-2205 changes.
247e5fa [Josh Rosen] Merge remote-tracking branch 'origin/master' into multi-way-join-planning-improvements
c57a954 [Yin Huai] Bug fix.
d3d2e64 [Yin Huai] First round of cleanup.
f9516b0 [Yin Huai] Style
c6667e7 [Yin Huai] Add PartitioningCollection.
e616d3b [Yin Huai] wip
7c2d2d8 [Yin Huai] Bug fix and refactoring.
69bb072 [Yin Huai] Introduce NullSafeHashPartitioning and NullUnsafePartitioning.
d5b84c3 [Yin Huai] Do not add unnessary filters.
2201129 [Yin Huai] Filter out rows that will not be joined in equal joins early.
This pull request creates two isOrderable functions in RowOrdering that can be used to check whether a data type or a sequence of expressions can be used in sorting.
Author: Reynold Xin <rxin@databricks.com>
Closes#7880 from rxin/SPARK-9546 and squashes the following commits:
f9e322d [Reynold Xin] Fixed tests.
0439b43 [Reynold Xin] [SPARK-9546][SQL] Centralize orderable data type checking.
The detailed approach is documented in UnsafeKVExternalSorterSuite.testKVSorter(), working as follows:
1. Create input by generating data randomly based on the given key/value schema (which is also randomly drawn from a list of candidate types)
2. Run UnsafeKVExternalSorter on the generated data
3. Collect the output from the sorter, and make sure the keys are sorted in ascending order
4. Sort the input by both key and value, and sort the sorter output also by both key and value. Compare the sorted input and sorted output together to make sure all the key/values match.
5. Check memory allocation to make sure there is no memory leak.
There is also a spill flag. When set to true, the sorter will spill probabilistically roughly every 100 records.
Author: Reynold Xin <rxin@databricks.com>
Closes#7873 from rxin/kvsorter-randomized-test and squashes the following commits:
a08c251 [Reynold Xin] Resource cleanup.
0488b5c [Reynold Xin] [SPARK-9543][SQL] Add randomized testing for UnsafeKVExternalSorter.
This pull request adds a destructAndCreateExternalSorter method to UnsafeFixedWidthAggregationMap. The new method does the following:
1. Creates a new external sorter UnsafeKVExternalSorter
2. Adds all the data into an in-memory sorter, sorts them
3. Spills the sorted in-memory data to disk
This method can be used to fallback to sort-based aggregation when under memory pressure.
The pull request also includes accounting fixes from JoshRosen.
TODOs (that can be done in follow-up PRs)
- [x] Address Josh's feedbacks from #7849
- [x] More documentation and test cases
- [x] Make sure we are doing memory accounting correctly with test cases (e.g. did we release the memory in BytesToBytesMap twice?)
- [ ] Look harder at possible memory leaks and exception handling
- [ ] Randomized tester for the KV sorter as well as the aggregation map
Author: Reynold Xin <rxin@databricks.com>
Author: Josh Rosen <joshrosen@databricks.com>
Closes#7860 from rxin/kvsorter and squashes the following commits:
986a58c [Reynold Xin] Bug fix.
599317c [Reynold Xin] Style fix and slightly more compact code.
fe7bd4e [Reynold Xin] Bug fixes.
fd71bef [Reynold Xin] Merge remote-tracking branch 'josh/large-records-in-sql-sorter' into kvsorter-with-josh-fix
3efae38 [Reynold Xin] More fixes and documentation.
45f1b09 [Josh Rosen] Ensure that spill files are cleaned up
f6a9bd3 [Reynold Xin] Josh feedback.
9be8139 [Reynold Xin] Remove testSpillFrequency.
7cbe759 [Reynold Xin] [SPARK-9531][SQL] UnsafeFixedWidthAggregationMap.destructAndCreateExternalSorter.
ae4a8af [Josh Rosen] Detect leaked unsafe memory in UnsafeExternalSorterSuite.
52f9b06 [Josh Rosen] Detect ShuffleMemoryManager leaks in UnsafeExternalSorter.
Generate prefix for DecimalType, fix the random generator of decimal
cc JoshRosen
Author: Davies Liu <davies@databricks.com>
Closes#7857 from davies/sort_decimal and squashes the following commits:
2433959 [Davies Liu] Merge branch 'master' of github.com:apache/spark into sort_decimal
de24253 [Davies Liu] fix style
0a54c1a [Davies Liu] sort decimal
When accessing a column in UnsafeRow, it's good to avoid the copy, then we should do deep copy when turn the UnsafeRow into generic Row, this PR brings generated FromUnsafeProjection to do that.
This PR also fix the expressions that cache the UTF8String, which should also copy it.
Author: Davies Liu <davies@databricks.com>
Closes#7840 from davies/avoid_copy and squashes the following commits:
230c8a1 [Davies Liu] address comment
fd797c9 [Davies Liu] Merge branch 'master' of github.com:apache/spark into avoid_copy
e095dd0 [Davies Liu] rollback rename
8ef5b0b [Davies Liu] copy String in Columnar
81360b8 [Davies Liu] fix class name
9aecb88 [Davies Liu] use FromUnsafeProjection to do deep copy for UTF8String and struct
This PR is based on #7643 , thanks to adrian-wang
Author: Davies Liu <davies@databricks.com>
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#7847 from davies/datediff and squashes the following commits:
74333d7 [Davies Liu] fix bug
22d8a8c [Davies Liu] optimize
85cdd21 [Davies Liu] remove unnecessary tests
241d90c [Davies Liu] Merge branch 'master' of github.com:apache/spark into datediff
e9dc0f5 [Davies Liu] fix datediff/to_utc_timestamp/from_utc_timestamp
c360447 [Daoyuan Wang] function datediff, to_utc_timestamp, from_utc_timestamp (commits merged)
This PR is based on #7208 , thanks to HuJiayin
Closes#7208
Author: HuJiayin <jiayin.hu@intel.com>
Author: Davies Liu <davies@databricks.com>
Closes#7850 from davies/initcap and squashes the following commits:
54472e9 [Davies Liu] fix python test
17ffe51 [Davies Liu] Merge branch 'master' of github.com:apache/spark into initcap
ca46390 [Davies Liu] Merge branch 'master' of github.com:apache/spark into initcap
3a906e4 [Davies Liu] implement title case in UTF8String
8b2506a [HuJiayin] Update functions.py
2cd43e5 [HuJiayin] fix python style check
b616c0e [HuJiayin] add python api
1f5a0ef [HuJiayin] add codegen
7e0c604 [HuJiayin] Merge branch 'master' of https://github.com/apache/spark into initcap
6a0b958 [HuJiayin] add column
c79482d [HuJiayin] support soundex
7ce416b [HuJiayin] support initcap rebase code
cc rxin
Author: Davies Liu <davies@databricks.com>
Closes#7856 from davies/sort_improve and squashes the following commits:
5fc81bd [Davies Liu] support DateType/TimestampType
This pull request adds a sortedIterator method to UnsafeFixedWidthAggregationMap that sorts its data in-place by the grouping key.
This is needed so we can fallback to external sorting for aggregation.
Author: Reynold Xin <rxin@databricks.com>
Closes#7849 from rxin/bytes2bytes-sorting and squashes the following commits:
75018c6 [Reynold Xin] Updated documentation.
81a8694 [Reynold Xin] [SPARK-9520][SQL] Support in-place sort in UnsafeFixedWidthAggregationMap.
This is based on #7641, thanks to zhichao-li
Closes#7641
Author: zhichao.li <zhichao.li@intel.com>
Author: Davies Liu <davies@databricks.com>
Closes#7848 from davies/substr and squashes the following commits:
461b709 [Davies Liu] remove bytearry from tests
b45377a [Davies Liu] Merge branch 'master' of github.com:apache/spark into substr
01d795e [zhichao.li] scala style
99aa130 [zhichao.li] add substring to dataframe
4f68bfe [zhichao.li] add binary type support for substring
This PR is based on #7581 , just fix the conflict.
Author: Cheng Hao <hao.cheng@intel.com>
Author: Davies Liu <davies@databricks.com>
Closes#7851 from davies/sort_array and squashes the following commits:
a80ef66 [Davies Liu] fix conflict
7cfda65 [Davies Liu] Merge branch 'master' of github.com:apache/spark into sort_array
664c960 [Cheng Hao] update the sort_array by using the ArrayData
276d2d5 [Cheng Hao] add empty line
0edab9c [Cheng Hao] Add asending/descending support for sort_array
80fc0f8 [Cheng Hao] Add type checking
a42b678 [Cheng Hao] Add sort_array support
This PR adds a `MapData` as internal representation of map type in Spark SQL, and provides a default implementation with just 2 `ArrayData`.
After that, we have specialized getters for all internal type, so I removed generic getter in `ArrayData` and added specialized `toArray` for it.
Also did some refactor and cleanup for `InternalRow` and its subclasses.
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#7799 from cloud-fan/map-data and squashes the following commits:
77d482f [Wenchen Fan] fix python
e8f6682 [Wenchen Fan] skip MapData equality check in HiveInspectorSuite
40cc9db [Wenchen Fan] add toString
6e06ec9 [Wenchen Fan] some more cleanup
a90aca1 [Wenchen Fan] add MapData
BytesToBytesMap current encodes key/value data in the following format:
```
8B key length, key data, 8B value length, value data
```
UnsafeExternalSorter, on the other hand, encodes data this way:
```
4B record length, data
```
As a result, we cannot pass records encoded by BytesToBytesMap directly into UnsafeExternalSorter for sorting. However, if we rearrange data slightly, we can then pass the key/value records directly into UnsafeExternalSorter:
```
4B key+value length, 4B key length, key data, value data
```
Author: Reynold Xin <rxin@databricks.com>
Closes#7845 from rxin/kvsort-rebase and squashes the following commits:
5716b59 [Reynold Xin] Fixed test.
2e62ccb [Reynold Xin] Updated BytesToBytesMap's data encoding to put the key first.
a51b641 [Reynold Xin] Added a KV sorter interface.
Add expression `sort_array` support.
Author: Cheng Hao <hao.cheng@intel.com>
This patch had conflicts when merged, resolved by
Committer: Davies Liu <davies.liu@gmail.com>
Closes#7581 from chenghao-intel/sort_array and squashes the following commits:
664c960 [Cheng Hao] update the sort_array by using the ArrayData
276d2d5 [Cheng Hao] add empty line
0edab9c [Cheng Hao] Add asending/descending support for sort_array
80fc0f8 [Cheng Hao] Add type checking
a42b678 [Cheng Hao] Add sort_array support
JIRA: https://issues.apache.org/jira/browse/SPARK-9415
Following up #7787. We shouldn't use MapType as grouping keys and join keys too.
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#7819 from viirya/map_join_groupby and squashes the following commits:
005ee0c [Liang-Chi Hsieh] For comments.
7463398 [Liang-Chi Hsieh] MapType can't be used as join keys, grouping keys.
This PR is based on #7533 , thanks to zhichao-li
Closes#7533
Author: zhichao.li <zhichao.li@intel.com>
Author: Davies Liu <davies@databricks.com>
Closes#7843 from davies/str_index and squashes the following commits:
391347b [Davies Liu] add python api
3ce7802 [Davies Liu] fix substringIndex
f2d29a1 [Davies Liu] Merge branch 'master' of github.com:apache/spark into str_index
515519b [zhichao.li] add foldable and remove null checking
9546991 [zhichao.li] scala style
67c253a [zhichao.li] hide some apis and clean code
b19b013 [zhichao.li] add codegen and clean code
ac863e9 [zhichao.li] reduce the calling of numChars
12e108f [zhichao.li] refine unittest
d92951b [zhichao.li] add lastIndexOf
52d7b03 [zhichao.li] add substring_index function
This patch creates a code generated unsafe row concatenator that can be used to concatenate/join two UnsafeRows into a single UnsafeRow.
Since it is inherently hard to test these low level stuff, the test suites employ randomized testing heavily in order to guarantee correctness.
Author: Reynold Xin <rxin@databricks.com>
Closes#7821 from rxin/rowconcat and squashes the following commits:
8717f35 [Reynold Xin] Rebase and code review.
72c5d8e [Reynold Xin] Fixed a bug.
a84ed2e [Reynold Xin] Fixed offset.
40c3fb2 [Reynold Xin] Reset random data generator.
f0913aa [Reynold Xin] Test fixes.
6687b6f [Reynold Xin] Updated documentation.
00354b9 [Reynold Xin] Support concat data as well.
e9a4347 [Reynold Xin] Updated.
6269f96 [Reynold Xin] Fixed a bug .
0f89716 [Reynold Xin] [SPARK-9358][SQL][WIP] Code generation for UnsafeRow concat.
This patch adds support for entries larger than the default page size in BytesToBytesMap. These large rows are handled by allocating special overflow pages to hold individual entries.
In addition, this patch integrates BytesToBytesMap with the ShuffleMemoryManager:
- Move BytesToBytesMap from `unsafe` to `core` so that it can import `ShuffleMemoryManager`.
- Before allocating new data pages, ask the ShuffleMemoryManager to reserve the memory:
- `putNewKey()` now returns a boolean to indicate whether the insert succeeded or failed due to a lack of memory. The caller can use this value to respond to the memory pressure (e.g. by spilling).
- `UnsafeFixedWidthAggregationMap. getAggregationBuffer()` now returns `null` to signal failure due to a lack of memory.
- Updated all uses of these classes to handle these error conditions.
- Added new tests for allocating large records and for allocations which fail due to memory pressure.
- Extended the `afterAll()` test teardown methods to detect ShuffleMemoryManager leaks.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#7762 from JoshRosen/large-rows and squashes the following commits:
ae7bc56 [Josh Rosen] Fix compilation
82fc657 [Josh Rosen] Merge remote-tracking branch 'origin/master' into large-rows
34ab943 [Josh Rosen] Remove semi
31a525a [Josh Rosen] Integrate BytesToBytesMap with ShuffleMemoryManager.
626b33c [Josh Rosen] Move code to sql/core and spark/core packages so that ShuffleMemoryManager can be integrated
ec4484c [Josh Rosen] Move BytesToBytesMap from unsafe package to core.
642ed69 [Josh Rosen] Rename size to numElements
bea1152 [Josh Rosen] Add basic test.
2cd3570 [Josh Rosen] Remove accidental duplicated code
07ff9ef [Josh Rosen] Basic support for large rows in BytesToBytesMap.
This PR brings SQL function soundex(), see https://issues.apache.org/jira/browse/HIVE-9738
It's based on #7115 , thanks to HuJiayin
Author: HuJiayin <jiayin.hu@intel.com>
Author: Davies Liu <davies@databricks.com>
Closes#7812 from davies/soundex and squashes the following commits:
fa75941 [Davies Liu] Merge branch 'master' of github.com:apache/spark into soundex
a4bd6d8 [Davies Liu] fix soundex
2538908 [HuJiayin] add codegen soundex
d15d329 [HuJiayin] add back ut
ded1a14 [HuJiayin] Merge branch 'master' of https://github.com/apache/spark
e2dec2c [HuJiayin] support soundex rebase code
Since code-gen is enabled by default, it is better to run window function tests with code-gen.
https://issues.apache.org/jira/browse/SPARK-9233
Author: Yin Huai <yhuai@databricks.com>
Closes#7832 from yhuai/SPARK-9233 and squashes the following commits:
4e4e4cc [Yin Huai] style
ca80e07 [Yin Huai] Test window function with codegen.
This PR enables the processing of multiple window frames in a single window operator. This should improve the performance of processing multiple window expressions wich share partition by/order by clauses, because it will be more efficient with respect to memory use and group processing.
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#7515 from hvanhovell/SPARK-8640 and squashes the following commits:
f0e1c21 [Herman van Hovell] Changed Window Logical/Physical plans to use partition by/order by specs directly instead of using WindowSpec.
e1711c2 [Herman van Hovell] Enabled the processing of multiple window frames in a single Window operator.
This PR address the comments in #7805
cc rxin
Author: Davies Liu <davies@databricks.com>
Closes#7817 from davies/trunc and squashes the following commits:
f729d5f [Davies Liu] rollback
cb7f7832 [Davies Liu] genCode() is protected
31e52ef [Davies Liu] fix style
ed1edc7 [Davies Liu] address comments for #7805
There lots of duplicated code in ternary expressions, create a TernaryExpression for them to reduce duplicated code.
cc chenghao-intel
Author: Davies Liu <davies@databricks.com>
Closes#7816 from davies/ternary and squashes the following commits:
ed2bf76 [Davies Liu] add TernaryExpression
This PR is based on #6988 , thanks to adrian-wang .
This brings two SQL functions: to_date() and trunc().
Closes#6988
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Author: Davies Liu <davies@databricks.com>
Closes#7805 from davies/to_date and squashes the following commits:
2c7beba [Davies Liu] Merge branch 'master' of github.com:apache/spark into to_date
310dd55 [Daoyuan Wang] remove dup test in rebase
980b092 [Daoyuan Wang] resolve rebase conflict
a476c5a [Daoyuan Wang] address comments from davies
d44ea5f [Daoyuan Wang] function to_date, trunc
While reviewing yhuai's patch for SPARK-2205 (#7773), I noticed that Exchange's `compatible` check may be incorrectly returning `false` in many cases. As far as I know, this is not actually a problem because the `compatible`, `meetsRequirements`, and `needsAnySort` checks are serving only as short-circuit performance optimizations that are not necessary for correctness.
In order to reduce code complexity, I think that we should remove these checks and unconditionally rewrite the operator's children. This should be safe because we rewrite the tree in a single bottom-up pass.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#7807 from JoshRosen/SPARK-9489 and squashes the following commits:
9d76ce9 [Josh Rosen] [SPARK-9489] Remove compatibleWith, meetsRequirements, and needsAnySort checks from Exchange
JIRA: https://issues.apache.org/jira/browse/SPARK-6319
Spark SQL uses plain byte arrays to represent binary values. However, the arrays are compared by reference rather than by values. Thus, we should not use BinaryType on Join and Aggregate in current implementation.
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#7787 from viirya/agg_no_binary_type and squashes the following commits:
4f76cac [Liang-Chi Hsieh] Throw AnalysisException when using BinaryType on Join and Aggregate.
This PR brings the support of DecimalType in UnsafeRow, for precision <= 18, it's settable, otherwise it's not settable.
Author: Davies Liu <davies@databricks.com>
Closes#7758 from davies/unsafe_decimal and squashes the following commits:
478b1ba [Davies Liu] address comments
536314c [Davies Liu] Merge branch 'master' of github.com:apache/spark into unsafe_decimal
7c2e77a [Davies Liu] fix JoinedRow
76d6fa4 [Davies Liu] fix tests
99d3151 [Davies Liu] Merge branch 'master' of github.com:apache/spark into unsafe_decimal
d49c6ae [Davies Liu] support DecimalType in UnsafeRow
Author: Reynold Xin <rxin@databricks.com>
Closes#7803 from rxin/SPARK-9458 and squashes the following commits:
5b032dc [Reynold Xin] Fix string.
b670dbb [Reynold Xin] [SPARK-9458][SPARK-9469][SQL] Code generate prefix computation in sorting & moves unsafe conversion out of TungstenSort.
This PR is based on #7589 , thanks to adrian-wang
Added SQL function date_add, date_sub, add_months, month_between, also add a rule for
add/subtract of date/timestamp and interval.
Closes#7589
cc rxin
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Author: Davies Liu <davies@databricks.com>
Closes#7754 from davies/date_add and squashes the following commits:
e8c633a [Davies Liu] Merge branch 'master' of github.com:apache/spark into date_add
9e8e085 [Davies Liu] Merge branch 'master' of github.com:apache/spark into date_add
6224ce4 [Davies Liu] fix conclict
bd18cd4 [Davies Liu] Merge branch 'master' of github.com:apache/spark into date_add
e47ff2c [Davies Liu] add python api, fix date functions
01943d0 [Davies Liu] Merge branch 'master' into date_add
522e91a [Daoyuan Wang] fix
e8a639a [Daoyuan Wang] fix
42df486 [Daoyuan Wang] fix style
87c4b77 [Daoyuan Wang] function add_months, months_between and some fixes
1a68e03 [Daoyuan Wang] poc of time interval calculation
c506661 [Daoyuan Wang] function date_add , date_sub
unix_timestamp(): long
Gets current Unix timestamp in seconds.
unix_timestamp(string|date): long
Converts time string in format yyyy-MM-dd HH:mm:ss to Unix timestamp (in seconds), using the default timezone and the default locale, return null if fail: unix_timestamp('2009-03-20 11:30:01') = 1237573801
unix_timestamp(string date, string pattern): long
Convert time string with given pattern (see [http://docs.oracle.com/javase/tutorial/i18n/format/simpleDateFormat.html]) to Unix time stamp (in seconds), return null if fail: unix_timestamp('2009-03-20', 'yyyy-MM-dd') = 1237532400.
from_unixtime(bigint unixtime[, string format]): string
Converts the number of seconds from unix epoch (1970-01-01 00:00:00 UTC) to a string representing the timestamp of that moment in the current system time zone in the format of "1970-01-01 00:00:00".
Jira:
https://issues.apache.org/jira/browse/SPARK-8174https://issues.apache.org/jira/browse/SPARK-8175
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#7644 from adrian-wang/udfunixtime and squashes the following commits:
2fe20c4 [Daoyuan Wang] util.Date
ea2ec16 [Daoyuan Wang] use util.Date for better performance
a2cf929 [Daoyuan Wang] doc return null instead of 0
f6f070a [Daoyuan Wang] address comments from davies
6a4cbb3 [Daoyuan Wang] temp
56ded53 [Daoyuan Wang] rebase and address comments
14a8b37 [Daoyuan Wang] function unix_timestamp, from_unixtime
JIRA: https://issues.apache.org/jira/browse/SPARK-9361
Currently, we call `aggregate.Utils.tryConvert` in many places to check it the logical.Aggregate can be run with new aggregation. But looks like `aggregate.Utils.tryConvert` will cost considerable time to run. We should only call `tryConvert` once and keep it value in `logical.Aggregate` and reuse it.
In `org.apache.spark.sql.execution.aggregate.Utils`, the codes involving with `tryConvert` should be moved to catalyst because it actually doesn't deal with execution details.
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#7677 from viirya/refactor_aggregate and squashes the following commits:
babea30 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into refactor_aggregate
9a589d7 [Liang-Chi Hsieh] Fix scala style.
0a91329 [Liang-Chi Hsieh] Refactor new aggregation code to reduce the times to call tryConvert.
Users can now get the file name of the partition being read in. A thread local variable is in `SQLNewHadoopRDD` and is set when the partition is computed. `SQLNewHadoopRDD` is moved to core so that the catalyst package can reach it.
This supports:
`df.select(inputFileName())`
and
`sqlContext.sql("select input_file_name() from table")`
Author: Joseph Batchik <josephbatchik@gmail.com>
Closes#7743 from JDrit/input_file_name and squashes the following commits:
abb8609 [Joseph Batchik] fixed failing test and changed the default value to be an empty string
d2f323d [Joseph Batchik] updates per review
102061f [Joseph Batchik] updates per review
75313f5 [Joseph Batchik] small fixes
c7f7b5a [Joseph Batchik] addeding input file name to Spark SQL
JIRA: https://issues.apache.org/jira/browse/SPARK-9428
Author: Yijie Shen <henry.yijieshen@gmail.com>
Closes#7748 from yjshen/string_cleanup and squashes the following commits:
e0c2b3d [Yijie Shen] update codegen in RegExpExtract and RegExpReplace
26614d2 [Yijie Shen] MathFunctionSuite
a402859 [Yijie Shen] complex_create, conditional and cast
6e4e608 [Yijie Shen] arithmetic and cast
52593c1 [Yijie Shen] null input test cases for StringExpressionSuite
Also we could create a Python UDT without having a Scala one, it's important for Python users.
cc mengxr JoshRosen
Author: Davies Liu <davies@databricks.com>
Closes#7453 from davies/class_in_main and squashes the following commits:
4dfd5e1 [Davies Liu] add tests for Python and Scala UDT
793d9b2 [Davies Liu] Merge branch 'master' of github.com:apache/spark into class_in_main
dc65f19 [Davies Liu] address comment
a9a3c40 [Davies Liu] Merge branch 'master' of github.com:apache/spark into class_in_main
a86e1fc [Davies Liu] fix serialization
ad528ba [Davies Liu] Merge branch 'master' of github.com:apache/spark into class_in_main
63f52ef [Davies Liu] fix pylint check
655b8a9 [Davies Liu] Merge branch 'master' of github.com:apache/spark into class_in_main
316a394 [Davies Liu] support Python UDT with UTF
0bcb3ef [Davies Liu] fix bug in mllib
de986d6 [Davies Liu] fix test
83d65ac [Davies Liu] fix bug in StructType
55bb86e [Davies Liu] support Python UDT in __main__ (without Scala one)
Author: Reynold Xin <rxin@databricks.com>
Closes#7767 from rxin/SPARK-9462 and squashes the following commits:
ef3e2d9 [Reynold Xin] Removed println
713ac3a [Reynold Xin] More unit tests.
bb5c334 [Reynold Xin] [SPARK-9462][SQL] Initialize nondeterministic expressions in code gen fallback mode.
In our existing sort prefix generation code, we use expression's eval method to generate the prefix, which results in object allocation for every prefix. We can use the specialized getters available on InternalRow directly to avoid the object allocation.
I also removed the FLOAT prefix, opting for converting float directly to double.
Author: Reynold Xin <rxin@databricks.com>
Closes#7763 from rxin/sort-prefix and squashes the following commits:
5dc2f06 [Reynold Xin] [SPARK-9458] Avoid object allocation in prefix generation.
We accidentally moved the list of expressions from the generated code instance to the class wrapper, and as a result, different threads are sharing the same set of expressions, which cause problems for expressions with mutable state.
This pull request fixed that problem, and also added unit tests for all codegen classes, except GeneratedOrdering (which will never need any expressions since sort now only accepts bound references.
Author: Reynold Xin <rxin@databricks.com>
Closes#7759 from rxin/SPARK-9448 and squashes the following commits:
c09b50f [Reynold Xin] [SPARK-9448][SQL] GenerateUnsafeProjection should not share expressions across instances.
We need to make page sizes configurable so we can reduce them in unit tests and increase them in real production workloads. These sizes are now controlled by a new configuration, `spark.buffer.pageSize`. The new default is 64 megabytes.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#7741 from JoshRosen/SPARK-9411 and squashes the following commits:
a43c4db [Josh Rosen] Fix pow
2c0eefc [Josh Rosen] Fix MAXIMUM_PAGE_SIZE_BYTES comment + value
bccfb51 [Josh Rosen] Lower page size to 4MB in TestHive
ba54d4b [Josh Rosen] Make UnsafeExternalSorter's page size configurable
0045aa2 [Josh Rosen] Make UnsafeShuffle's page size configurable
bc734f0 [Josh Rosen] Rename configuration
e614858 [Josh Rosen] Makes BytesToBytesMap page size configurable
We want to introduce a new IntervalType in 1.6 that is based on only the number of microseoncds,
so interval can be compared.
Renaming the existing IntervalType to CalendarIntervalType so we can do that in the future.
Author: Reynold Xin <rxin@databricks.com>
Closes#7745 from rxin/calendarintervaltype and squashes the following commits:
99f64e8 [Reynold Xin] One more line ...
13466c8 [Reynold Xin] Fixed tests.
e20f24e [Reynold Xin] [SPARK-9430][SQL] Rename IntervalType to CalendarIntervalType.
Author: Reynold Xin <rxin@databricks.com>
Closes#7747 from rxin/SPARK-9127 and squashes the following commits:
e851418 [Reynold Xin] [SPARK-9127][SQL] Rand/Randn codegen fails with long seed.
as an offline discussion with rxin , it's weird to be computing stuff while doing sorting, we should only order by bound reference during execution.
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#7593 from cloud-fan/sort and squashes the following commits:
7b1bef7 [Wenchen Fan] add test
daf206d [Wenchen Fan] add more comments
289bee0 [Wenchen Fan] do not order by expressions which still need evaluation
Right now, we use double to parse all the float number in SQL. When it's used in expression together with DecimalType, it will turn the decimal into double as well. Also it will loss some precision when using double.
This PR change to parse float number to decimal or double, based on it's using scientific notation or not, see https://msdn.microsoft.com/en-us/library/ms179899.aspx
This is a break change, should we doc it somewhere?
Author: Davies Liu <davies@databricks.com>
Closes#7642 from davies/parse_decimal and squashes the following commits:
1f576d9 [Davies Liu] Merge branch 'master' of github.com:apache/spark into parse_decimal
5e142b6 [Davies Liu] fix scala style
eca99de [Davies Liu] fix tests
2afe702 [Davies Liu] Merge branch 'master' of github.com:apache/spark into parse_decimal
f4a320b [Davies Liu] Update SqlParser.scala
1c48e34 [Davies Liu] use decimal or double when parsing SQL
We will do local projection for LocalRelation, and thus reuse the same Expression object among multiply evaluations. We should reset the mutable states of Expression before evaluate it.
Fix `PullOutNondeterministic` rule to make it work for `Sort`.
Also got a chance to cleanup the dataframe test suite.
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#7674 from cloud-fan/show and squashes the following commits:
888934f [Wenchen Fan] fix sort
c0e93e8 [Wenchen Fan] local DataFrame with random columns should return same value when call `show`
https://issues.apache.org/jira/browse/SPARK-9422
Author: Yin Huai <yhuai@databricks.com>
Closes#7737 from yhuai/removePlaceHolder and squashes the following commits:
ec29b44 [Yin Huai] Remove placeholder attributes.
UnsafeRow.getDouble and getFloat() return NaN when called on columns that are null, which is inconsistent with the behavior of other row classes (which is to return 0.0).
In addition, the generic get(ordinal, dataType) method should always return null for a null literal, but currently it handles nulls by calling the type-specific accessors.
This patch addresses both of these issues and adds a regression test.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#7736 from JoshRosen/unsafe-row-null-fixes and squashes the following commits:
c8eb2ee [Josh Rosen] Fix test in UnsafeRowConverterSuite
6214682 [Josh Rosen] Fixes to null handling in UnsafeRow
Since catalyst package already depends on Spark core, we can move those expressions
into catalyst, and simplify function registry.
This is a followup of #7478.
Author: Reynold Xin <rxin@databricks.com>
Closes#7735 from rxin/SPARK-8003 and squashes the following commits:
2ffbdc3 [Reynold Xin] [SPARK-8003][SQL] Move expressions in sql/core package to catalyst.
The original patch didn't handle nulls correctly for next_day.
Author: Reynold Xin <rxin@databricks.com>
Closes#7718 from rxin/next_day and squashes the following commits:
616a425 [Reynold Xin] Merged DatetimeExpressionsSuite into DateFunctionsSuite.
faa78cf [Reynold Xin] Merged DatetimeFunctionsSuite into DateExpressionsSuite.
6c4fb6a [Reynold Xin] [SPARK-8196][SQL] Fix null handling & documentation for next_day.
Author: Reynold Xin <rxin@databricks.com>
Closes#7720 from rxin/struct-followup and squashes the following commits:
d9757f5 [Reynold Xin] [SPARK-9373][SQL] follow up for StructType support in Tungsten projection.
Both expressions already implement code generation.
Author: Reynold Xin <rxin@databricks.com>
Closes#7723 from rxin/abs-formatnum and squashes the following commits:
31ed765 [Reynold Xin] [SPARK-9402][SQL] Remove CodegenFallback from Abs / FormatNumber.
Our CodeFormatter currently does not handle parentheses, and as a result in code dump, we see code formatted this way:
```
foo(
a,
b,
c)
```
With this patch, it is formatted this way:
```
foo(
a,
b,
c)
```
Author: Reynold Xin <rxin@databricks.com>
Closes#7712 from rxin/codeformat-parentheses and squashes the following commits:
c2b1c5f [Reynold Xin] Took square bracket out
3cfb174 [Reynold Xin] Code review feedback.
91f5bb1 [Reynold Xin] [SPARK-9394][SQL] Handle parentheses in CodeFormatter.
This is actually contains 3 minor issues:
1) Enable the unit test(codegen) for mutable expressions (FormatNumber, Regexp_Replace/Regexp_Extract)
2) Use the `PlatformDependent.copyMemory` instead of the `System.arrayCopy`
Author: Cheng Hao <hao.cheng@intel.com>
Closes#7566 from chenghao-intel/codegen_ut and squashes the following commits:
24f43ea [Cheng Hao] enable codegen for mutable expression & UTF8String performance
